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- SPA Generative Model for Review-Based Recommendations
by Oren Sar Shalom, Guy Uziel, Amir Kantor
User generated reviews is a highly informative source of information, that has recently gained lots of attention in the recommender systems community. In this work we propose a generative latent variable model that explains both observed ratings and textual reviews. This latent variable model allows to combine any traditional collaborative filtering method, together with any deep learning architecture for text processing. Experimental results on four benchmark datasets demonstrate its superiority comparing to all baseline recommender systems. Furthermore, a running time analysis shows that this approach is in order of magnitude faster that relevant baselines.
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Poster
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- SPA Simple Multi-Armed Nearest-Neighbor Bandit for Interactive Recommendation
by Javier Sanz-Cruzado, Pablo Castells, Esther López
The cyclic nature of the recommendation task is being increasingly taken into account in recommender systems research. In this line, framing interactive recommendation as a genuine reinforcement learning problem, multi-armed bandit approaches have been increasingly considered as a means to cope with the dual exploitation/exploration goal of recommendation. In this paper we develop a simple multi-armed bandit elaboration of neighbor-based collaborative filtering. The approach can be seen as a variant of the nearest-neighbors scheme, but endowed with a controlled stochastic exploration capability of the users’ neighborhood, by a parameter-free application of Thompson sampling. Our approach is based on a formal development and a reasonably simple design, whereby it aims to be easy to reproduce and further elaborate upon. We report experiments using datasets from different domains showing that neighbor-based bandits indeed achieve recommendation accuracy enhancements in the mid to long run.
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Poster
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- SPAdversarial Tensor Factorization for Context-aware Recommendation
by Huiyuan Chen, Jing Li
Contextual factors such as time, location, or tag, can affect user preferences for a particular item. Context-aware recommendations are thus critical to improve both quality and explainability of recommender systems, compared to traditional recommendations solely based on user-item interactions. Tensor factorization machines have achieved state-of-the-art performance due to their generic integration of users, items, and contextual factors in one unify way. However, few work has focused on the robustness of a context-aware recommender system. Improving the robustness of a tensor-based model is challenging due to the sparsity of the observed tensor and the multi-linear nature of tensor factorization. In this paper, we propose ATF, a model that combines tensor factorization and adversarial learning for context-aware recommendations. Doing so allows us to reap the benefits of tensor factorization, while enhancing the robustness of a recommender model, and thus improves its performance. Empirical studies on two real-world datasets show that the proposed method outperforms standard tensor-based methods.
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Poster
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- SPAligning Daily Activities with Personality: Towards A Recommender System for Improving Wellbeing
by Mohammed Khwaja, Miquel Ferrer, Jesus Omana Iglesias, A. Aldo Faisal, Aleksandar Matic
Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are ‘good’ or ‘bad’ (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.
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Poster
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- SPAsymmetric Bayesian Personalized Ranking for One-Class Collaborative Filtering
by Shan Ouyang, Lin Li, Weike Pan, Zhong Ming
In this paper, we propose a novel preference assumption for modeling users’ one-class feedback such as ‘thumb up’ in an important recommendation problem called one-class collaborative filtering (OCCF). Specifically, we address a fundamental limitation of a recent symmetric pairwise preference assumption and propose a novel and first asymmetric one, which is able to make the preferences of different users more comparable. With the proposed asymmetric pairwise preference assumption, we further design a novel recommendation algorithm called asymmetric Bayesian personalized ranking (ABPR). Extensive empirical studies on two large and public datasets show that our ABPR performs significantly better than several state-of-the-art recommendation methods with either pointwise preference assumption or pairwise preference assumption.
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Poster
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- SPAttribute-based Evaluation for Recommender Systems: Incorporating User and Item Attributes in Evaluation Metrics
by Pablo Sánchez, Alejandro Bellogín
Research in Recommender Systems evaluation remains critical to study the efficiency of developed algorithms. Even if different aspects have been addressed and some of its shortcomings — such as biases, robustness, or cold start — have been analyzed and solutions or guidelines have been proposed, there are still some gaps that need to be further investigated. At the same time, the increasing amount of data collected by most recommender systems allows to gather valuable information from users and items which is being neglected by classical offline evaluation metrics. In this work, we integrate such information into the evaluation process in two complementary ways: on the one hand, we aggregate any evaluation metric according to the groups defined by the user attributes, and, on the other hand, we exploit item attributes to consider some recommended items as surrogates of those interacted by the user, with a proper penalization. Our results evidence that this novel evaluation methodology allows to capture different nuances of the algorithms performance, inherent biases in the data, and even fairness of the recommendations.
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- SPCombining Text Summarization and Aspect-based Sentiment Analysis of Users’ Reviews to Justify Recommendations
by Cataldo Musto, Gaetano Rossiello, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro
In this paper we present a methodology to justify recommendations that relies on the information extracted from users’ reviews discussing the available items. The intuition behind the approach is to conceive the justification as a summary of the most relevant and distinguishing aspects ofthe item, automatically obtained by analyzing the available reviews. To this end, we designed a pipeline of natural language processing techniques based on aspect extraction, sentiment analysis and text summarization to gather the reviews, process the relevant excerpts,and generate a unique synthesis presenting the main characteristics of the item. Such a summary is finally presented to the target user as justification of the recommendation she received. In the experimental evaluation we carried out a user study in the movie domain (N=141) and the results showed that our approach is able to make the recommendation process more transparent, engaging and trustful for the users. Moreover, the proposed method also beat another review-based explanation technique, thus confirming the validity of our intuition.
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Poster
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- SPCompositional Network Embedding for Link Prediction
by Tianshu Lyu, Fei Sun, Peng Jiang, Wenwu Ou, Yan Zhang
Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the existing methods from being applied in real cases. Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem. The heterogeneous network usually requires extra work to encode node types, as node type is not able to be identified by node ID. Node ID carries rare information, resulting in the criticism that the existing methods are not robust to noise. To address this issue, we introduce Compositional Network Embedding, a general inductive network representation learning framework that generates node embeddings by combining node features based on the ‘principle of compositionally’. Instead of directly optimizing an embedding lookup based on arbitrary node IDs, we learn a composition function that infers node embeddings by combining the corresponding node attribute embeddings through a graph-based loss. For evaluation, we conduct the experiments on link prediction under four different settings. The results verified the effectiveness and generalization ability of compositional network embeddings, especially on unseen nodes.
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- SPData Mining for Item Recommendation in MOBA Games
by Vladimir Araujo, Felipe Rios, Denis Parra
E-Sports has been positioned as an important activity within MOBA (Multiplayer Online Battle Arena) games in recent years. There is existing research on recommender systems in this topic, but most of it focuses on the character recommendation problem. However, the recommendation of items is also challenging because of its contextual nature, depending on the other characters. We have developed a framework that suggests items for a character based on the match context. The system aims to help players who have recently started the game as well as frequent players to take strategic advantage during a match and to improve their purchasing decision making. By analyzing a dataset of ranked matches through data mining techniques, we can capture purchase dynamic of experienced players to use it to generate recommendations. The results show that our proposed solution yields up to 80% of mAP, suggesting that the method leverages context information successfully. These results, together with open issues we mention in the paper, call for further research in the area.
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Poster
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- SPDualDiv: Diversifying Items and Explanation Styles in Explainable Hybrid Recommendation
by Kosetsu Tsukuda, Masataka Goto
In recommender systems, item diversification and explainable recommendations improve users’ satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.
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Poster
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- SPEnhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
by Daeryong Kim, Bongwon Suh
Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model’s inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix).
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Poster
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- SPFind My Next Job Labor Market Recommendations Using Administrative Big Data
by Snorre S. Frid-Nielsen
Labor markets are undergoing change due to factors such as automatization and globalization, motivating the development of occupational recommender systems for jobseekers and caseworkers. This study generates occupational recommendations by utilizing a novel data set consisting of administrative records covering the entire Danish workforce. Based on actual labor market behavior in the period 2012-2015, how well can different models predict each users’ next occupation in 2016? Through offline experiments, the study finds that gradient-boosted decision tree models provide the best recommendations for future occupations in terms of mean reciprocal ranking and recall. Further, gradient-boosted decision tree models offer distinct advantages in the labor market domain due to their interpretability and ability to harness additional background information on workers. However, the study raises concerns regarding trade-offs between model accuracy and ethical issues, including privacy and the social reinforcement of gender divides.
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- SPOFrom Preference into Decision Making: Modeling User Interactions in Recommender Systems
by Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, Joseph A. Konstan
User-system interaction in recommender systems involves three aspects: temporal browsing (viewing recommendation lists and/or searching/filtering), action (performing actions on recommended items, e.g., clicking, consuming) and inaction (neglecting or skipping recommended items). Modern recommenders build machine learning models from recordings of such user interaction with the system, and in doing so they commonly make certain assumptions (e.g., pairwise preference orders, independent or competitive probabilistic choices, etc.). In this paper, we set out to study the effects of these assumptions along three dimensions in eight different single models and three associated hybrid models on a user browsing data set collected from a real-world recommender system application. We further design a novel model based on recurrent neural networks and multi-task learning, inspired by Decision Field Theory, a model of human decision making. We report on precision, recall, and MAP, finding that this new model outperforms the others.
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Paper Session 1: Ranking and Deep Learning in Recommenders
Slides
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- SPGreedy Optimized Multileaving for Personalization
by Kojiro Iizuka, Takeshi Yoneda, Yoshifumi Seki
Personalization plays an important role in many services. To evaluate personalized rankings, online evaluation, such as A/B testing, is widely used today. Recently, multileaving has been found to be an efficient method for evaluating rankings in information retrieval fields. This paper describes the first attempt to optimize the multileaving method for personalization settings. We clarify the challenges of applying this method to personalized rankings. Then, to solve these challenges, we propose greedy optimized multileaving (GOM) with a new credit feedback function. The empirical results showed that GOM was stable for increasing ranking lengths and the number of rankers. We implemented GOM on our actual news recommender systems, and compared its online performance. The results showed that GOM evaluated the personalized rankings precisely, with significantly smaller sample sizes (< 1/10) than A/B testing.
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Poster
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- SPGuiding Creative Design in Online Advertising
by Shaunak Mishra, Manisha Verma, Jelena Gligorijevic
Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers)typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspirationfrom prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand’s ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.
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- SPHow Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations
by Helma Torkamaan, Catalin-Mihai Barbu, Jürgen Ziegler
Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users’ feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users’ expectations of‚ and their trust in, RS.
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Poster
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- SPLatent Multi-Criteria Ratings for Recommendations
by Pan Li, Alexander Tuzhilin
Multi-Criteria Recommender systems have been increasingly valuable for helping the consumers identify the most relevant items with their multi-criteria feedback along different dimensions of user experiences. However, previous design of multi-criteria recommendation algorithms did not take into account user reviews. This is unfortunate because multi-criteria recommender systems suffer from the missing ratings problem and user reviews could be helpful for alleviating the missing ratings and sparsity problem, thus improving the quality of recommendations. Besides, it’s not clear from prior literature how to select the most important criteria to collect from users. In addition, previously proposed methods did not consider the latent semantic relations between users and items. To address these concerns, in this paper we propose a novel design of multi-criteria recommendation based on latent multi-criteria ratings generated from user reviews. In particular, we utilize variational autoencoders to map user reviews into latent embeddings, which are subsequently compressed into smaller dimensional discrete vectors using the Gumbel-Softmax reparameterization technique. The resulting compressed vectors constitute latent multi-criteria ratings that we use for the recommendation purposes via standard multi-criteria recommendation methods. We show that the proposed latent multi-criteria rating approach outperforms several baselines significantly and consistently across different datasets and performance evaluation measures.
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- SPMulti-Armed Recommender System Bandit Ensembles
by Rocío Cañamares, Marcos Redondo, Pablo Castells
It has long been found that well-configured recommender system ensembles can achieve better effectiveness than the combined systems separately. Sophisticated approaches have been developed to automatically optimize the ensembles’ configuration to maximize their performance gains. However most work in this area has targeted simplified scenarios where algorithms are tested and compared on a single non-interactive run. In this paper we consider a more realistic perspective bearing in mind the cyclic nature of the recommendation task, where a large part of the system’s input is collected from the reaction of users to the recommendations they are delivered. The cyclic process provides the opportunity for ensembles to observe and learn about the effectiveness of the combined algorithms, and improve the ensemble configuration progressively. In this paper we explore the adaptation of a multi-armed bandit approach to achieve this, by representing the combined systems as arms, and the ensemble as a bandit that at each step selects an arm to produce the next round of recommendations. We report experiments showing the effectiveness of this approach compared to ensembles that lack the iterative perspective. Along the way, we find illustrative pitfall examples that can result from common, single-shot offline evaluation setups.
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Poster
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- SPMusic Recommendations in Hyperbolic Space: An Application of Empirical Bayes and Hierarchical Poincaré Embeddings
by Timothy Schmeier, Sam Garrett, Joseph Chisari, Brett Vintch
Matrix Factorization (MF) is a common method for generating recommendations, where the proximity of entities like users or items in the embedded space indicates their similarity to one another. Though almost all applications implicitly use a Euclidean embedding space to represent two entity types, recent work has suggested that a hyperbolic Poincare ball may be more well suited to representing multiple entity types, and in particular, hierarchies. We describe a novel method to embed a hierarchy of related music entities in hyperbolic space. We also describe how a parametric empirical Bayes approach can be used to estimate link reliability between entities in the hierarchy. Applying these methods together to build personalized playlists for users in a digital music service yielded a large and statistically significant increase in performance during an A/B test, as compared to the Euclidean model.
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- SPOn Gossip-based Information Dissemination in Pervasive Recommender Systems
by Tobias Eichinger, Felix Beierle, Robin Papke, Lucas Rebscher, Hong Chinh Tran, Magdalena Trzeciak
Pervasive computing systems employ distributed and embedded devices in order to raise, communicate, and process data in an anytime-anywhere fashion. Certainly, its most prominent device is the smartphone due to its wide proliferation, growing computation power, and wireless networking capabilities. In this context, we revisit the implementation of digitalized word-of-mouth that suggests exchanging item preferences between smartphones offline and directly in immediate proximity. Collaboratively and decentrally collecting data in this way has two benefits. First, it allows to attach for instance location-sensitive context information in order to enrich collected item preferences. Second, model building does not require network connectivity. Despite the benefits, the approach naturally raises data privacy and data scarcity issues. In order to address both, we propose Propagate and Filter, a method that translates the traditional approach of finding similar peers and exchanging item preferences among each other from the field of decentralized to that of pervasive recommender systems. Additionally, we present preliminary results on a prototype mobile application that implements the proposed device-to-device information exchange. Average ad-hoc connection delays of 25.9 seconds and reliable connection success rates within 6 meters underpin the approach’s technical feasibility.
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Poster
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- SPOn the Discriminative power of Hyper-parameters in Cross-Validation and How to Choose Them
by Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Claudio Pomo, Azzurra Ragone
Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As an example, it may affect not just accuracy but also novelty as well as it may depend on the adopted dataset. Moreover, sometimes it could be sufficient to concentrate on a single parameter only (or a few of them) instead of their overall set. In this paper we report on our investigation on hyper-parameters tuning by performing an extensive 10-Folds Cross-Validation on MovieLens and Amazon Movies for three well-known baselines: User-kNN, Item-kNN, BPR-MF. We adopted a grid search strategy considering approximately 15 values for each parameter, and we then evaluated each combination of parameters in terms of accuracy and novelty. We investigated the discriminative power of nDCG, Precision, Recall, MRR, EFD, EPC, and, finally, we analyzed the role of parameters on model evaluation for Cross-Validation.
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Poster
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- SPOPace My Race: Recommendations for Marathon Running
by Jakim Berndsen, Barry Smyth, Aonghus Lawlor
We propose marathon running as a novel domain for recommender systems and machine learning. Using high-resolution marathon performance data from multiple marathon races (n=7931), we build in-race recommendations for runners. We show that we can outperform the existing techniques which are currently employed for in-race finish-time prediction, and we demonstrate how such predictions may be used to make real time recommendations to runners. The recommendations are made at critical points in the race to provide personalised guidance so the runner can adjust their race strategy. Through the association of model features and the expert domain knowledge of marathon runners we generate explainable, adaptable pacing recommendations which can guide runners to their best possible finish time and help them avoid the potentially catastrophic effects of hitting the wall.
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Paper Session 5: Applications of Recommenders in Personal Needs
Slides
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- SPPAL: A Position-bias Aware Learning Framework for CTR Prediction in Live Recommender Systems
by Huifeng Guo, Jinkai Yu, Qing Liu, Ruiming Tang, Yuzhou Zhang
Predicting Click-Through Rate (CTR) accurately is crucial in recommender systems. In general, a CTR model is trained based on user feedback which is collected from offline traffic logs. However, position-bias exists in user feedback because a user clicks on an item may not only because she favors it but also because it is in a good position. One way is to model position as a feature in the training data, which is widely used in industrial applications due to its simplicity. Specifically, a default position value has to be used to predict CTR in online inference since the actual position information is not available at that time. However, using different default position values may result in completely different recommendation results. As a result, this approach leads to sub-optimal online performance. To address this problem, in this paper, we propose a Position-bias Aware Learning framework (PAL) for CTR prediction in a live recommender system. It is able to model the position-bias in offline training and conduct online inference without position information. Extensive online experiments are conducted to demonstrate that PAL outperforms the baselines by 3% – 35% in terms of CTR and CVR in a three-week AB test.
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- SPPDMFRec: A Decentralised Matrix Factorisation with Tunable User-centric Privacy
by Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor
Conventional approaches to matrix factorisation (MF) typically rely on a centralised collection of user data for building a MF model. This approach introduces an increased risk when it comes to user privacy. In this short paper we propose an alternative, user-centric, privacy enhanced, decentralised approach to MF. Our method pushes the computation of the recommendation model to the user’s device, and eliminates the need to exchange sensitive personal information; instead only the loss gradients of local device-based) MF models need to be shared. Moreover, users can select the amount and type of information to be shared, for enhanced privacy. We demonstrate the effectiveness of this approach by considering different levels of user privacy in comparison with state-of-the-art alternatives.
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- SPPerformance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation
by Malte Ludewig, Noemi Mauro, Sara Latifi, Dietmar Jannach
The benefits of neural approaches are undisputed in many application areas. However, today’s research practice in applied machine learning‚ where researchers often use a variety of baselines, datasets, and evaluation procedures, can make it difficult to understand how much progress is actually achieved through novel technical approaches. In this work, we focus on the fast-developing area of session-based recommendation and aim to contribute to a better understanding of what represents the state-of-the-art. To that purpose, we have conducted an extensive set of experiments, using a variety of datasets, in which we benchmarked four neural approaches that were published in the last three years against each other and against a set of simpler baseline techniques, e.g., based on nearest neighbors. The evaluation of the algorithms under the exact same conditions revealed that the benefits of applying today’s neural approaches to session-based recommendations are still limited. In the majority of the cases, and in particular when precision and recall are used, it turned out that simple techniques in most cases outperform recent neural approaches. Our findings therefore point to certain major limitations of today’s research practice. By sharing our evaluation framework publicly, we hope that some of these limitations can be overcome in the future.
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- SPPersonalized Fairness-aware Re-ranking for Microlending
by Weiwen Liu, Jun Guo, Nasim Sonboli, Robin Burke, Shengyu Zhang
Microlending can lead to improved access to capital in impoverished countries. Recommender systems could be used in microlending to provide efficient and personalized service to lenders. However, increasing concerns about discrimination in machine learning hinder the application of recommender systems to the microfinance industry. Most previous recommender systems focus on pure personalization, with fairness issue largely ignored. A desirable fairness property in microlending is to give borrowers from different demographic groups a fair chance of being recommended, as stated by Kiva. To achieve this goal, we propose a Fairness-Aware Re-ranking (FAR) algorithm to balance ranking quality and borrower-side fairness. Furthermore, we take into consideration that lenders may differ in their receptivity to the diversification of recommended loans, and develop a Personalized Fairness-Aware Re-ranking (PFAR) algorithm. Experiments on a real-world dataset from Kiva.org show that our re-ranking algorithm can significantly promote fairness with little sacrifice in accuracy, and be attentive to individual lender preference on loan diversity.
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Poster
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- SPPick & Merge: An Efficient Item Filtering Scheme for Windows Store Recommendations
by Adi Makmal, Jonathan Ephrath, Hilik Berezin, Liron Allerhand, Nir Nice,Noam Koenigstein,
Microsoft Windows is the most popular operating system (OS) for personal computers (PCs). With hundreds of millions of users, its app marketplace, Windows Store, is one of the largest in the world. As such, special considerations are required in order to improve online computational efficiency and response times. This paper presents the results of an extensive research of effective filtering method for semi-personalized recommendations. The filtering problem, defined here for the first time, addresses an aspect that was so far largely overlooked by the recommender systems literature, namely effective and efficient method for removing items from semi-personalized recommendation lists. Semi-personalized recommendation lists serve a common list to a group of people based on their shared interest or background. Unlike fully personalized lists, these lists are cacheable and constitute the majority of recommendation lists in many online stores. This motivates the following question: can we remove (most of) the users’ undesired items without collapsing onto fully personalized recommendations?Our solution is based on dividing the users into few subgroups, such that each subgroup receives a different variant of the original recommendation list. This approach adheres to the principles of semi-personalization and hence preserves simplicity and cacheability. We formalize the problem of finding optimal subgroups that minimize the total number of filtering errors, and show that it is combinatorially formidable. Consequently, a greedy algorithm is proposed that filters out most of the undesired items, while bounding the maximal number of errors for each user. Finally, a detailed evaluation of the proposed algorithm is presented using both proprietary and public datasets.
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- SPOPredictability Limits in Session-based Next Item Recommendation
by Priit Järv
Session-based recommendations are based on the user’s recent actions, for example, the items they have viewed during the current browsing session or the sightseeing places they have just visited. Closely related is sequence-aware recommendation, where the choice of the next item should follow from the sequence of previous actions. We study seven benchmarks for session-based recommendation, covering retail, music and news domains to investigate how accurately user behavior can be predicted from the session histories. We measure the entropy rate of the data and estimate the limit of predictability to be between 44% and 73% in the included datasets. We establish some algorithm-specific limits on prediction accuracy for Markov chains, association rules and k-nearest neighbors methods. With most of the analyzed methods, the algorithm design limits their performance with sparse training data. The session based k-nearest neighbors are least restricted in comparison and have room for improvement across all of the analyzed datasets.
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Paper Session 3: Deep Learning for Recommender Systems
Poster
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- SPPredicting Online Performance of Job Recommender Systems With Offline Evaluation
by Adrien Mogenet, Tuan Anh Nguyen Pham, Masahiro Kazama, Jialin Kong
Recommender systems can be used to recommend jobs. In this context, implicit and explicit feedback signals we can collect are rare events, making the task of evaluation more complex. Online evaluation (A-B testing) is usually the most reliable way to measure the results from our experiments, but it is a slow process. In contrast, the offline evaluation process is faster, but it is critical to make it reliable as it informs our decision to roll out new improvements in production. In this paper, we review the comparative offline and online performances of three recommendations models, we describe the evaluation metrics we use and analyze how the offline performance metrics correlate with online metrics to understand how an offline evaluation process can be leveraged to inform the decisions.
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Poster
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- SPPredicting User Routines with Masked Dilated Convolutions
by Renzhong Wang, Dragomir Yankov, Michael R. Evans, Senthil Palanisamy, Siddhartha Arora, Wei Wu
Predicting users daily location visits – when and where they will go, and how long they will stay – is key for making effective location-based recommendations. Knowledge of an upcoming day allows the suggestion of relevant alternatives (e.g., a new coffee shop on the way to work) in advance, prior to a visit. This helps users make informed decisions and plan accordingly. People’s visit routines, or just routines, can vary significantly from day to day, and visits from earlier in the day, week, or month may affect subsequent choices. Traditionally, routine prediction has been modeled with sequence methods, such as HMMs or more recently with RNN-based architectures. However, the problem with such architectures is that their predictive performance degrades when increasing the number of historical observations in the routine sequence. In this paper, we propose Masked-TCN (MTCN), a novel method based on time-dilated convolutional networks. The method implements custom dilations and masking which can process effectively long routine sequences, identifying recurring patterns at different resolution – hourly, daily, weekly, monthly. We demonstrate that MTCN achieves 8% improvement in accuracy over current state-of-the-art solutions on a large data set of visit routines.
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- SPProduct Collection Recommendation in Online Retail
by Pigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Nian Yan, Unaiza Ahsan, Khalifeh Al Jadda, Huiming Qu
Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form “users who bought product A also bought products B, C, and D”. Although such recommendations can be useful, there is no guarantee that products A, B, C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction. We extend on traditional approaches that use mostly transactional data by incorporating both domain knowledge from product suppliers in the form of hierarchies, as well as textual attributes from the products. Our approach starts by combining product hierarchies together with transactional data or domain knowledge to identify candidate sets of product collections. Then, it generates the product collection recommendations from these candidate sets by learning a deep similarity model that leverages textual attributes. Experimental evaluation on real data from the Home Depot online retailer shows that the proposed solution can recommend collections of products with increased accuracy when compared to expert-crafted collections.
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Poster
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- SPPyRecGym: A Reinforcement Learning Gym for Recommender Systems
by Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor
Recommender systems (RS) share many features and objectives with reinforcement learning (RL) systems. The former aim to maximise user satisfaction by recommending the right items to the right users at the right time, the latter maximise future rewards by selecting state-changing actions in some environment. The concept of an RL gym has become increasingly important when it comes to supporting the development of RL models. A gym provides a simulation environment in which to test and develop RL agents, providing a state model, actions, rewards/penalties etc. In this paper we describe and demonstrate the PyRecGym gym, which is specifi- cally designed for the needs of recommender systems research, by supporting standard test datasets (MovieLens, Yelp etc.), common input types (text, numeric etc.), and thereby offering researchers a reproducible research environment to accelerate experimentation and development of RL in RS.
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- SPShould we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
by Emanuel Lacic, Markus Reiter-Haas, Tomislav Duricic, Valentin Slawicek, Elisabeth Lex
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian job platform Studo Jobs, we evaluate two popular recommendation scenarios: (i) providing similar jobs and, (ii) personalizing the job postings that are shown on the homepage. Our results show that for recommending similar jobs, we achieve the best online performance in terms of Click-Through Rate when we employ embeddings based on the most recent interaction. To personalize the job postings shown on a user’s homepage, however, combining embeddings based on the frequency and recency with which a user interacts with job postings results in the best online performance.
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Poster
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- SPThe Influence of Personal Values on Music Taste: Towards Value-Based Music Recommendations
by Sandy Manolios, Alan Hanjalic, Cynthia C. S. Liem
The field of recommender systems has a lot to gain from the field of psychology. Indeed, many psychology researchers have investigated relations between models that describe humans and consumption preferences. One example of this is personality, which has been shown to be a valid construct to describe people. As a consequence, personality-based recommenders have already proven to be a lead toward improving recommendations, by adapting them to their users’ traits. Beyond personality, there are more ways to describe a person’s identity. One of these ways is to consider personal values: what is important for the users in life at the most abstract level. Being complementary to personality traits, values may give another lead towards better user understanding. In this paper, we investigate this, taking music as a use case. We use a marketing interview technique to elicit 22 users’ personal values connected to their musical preferences. We show that personal values indeed play a role in people’s music preferences, and are the first to propose a map linking personal values to music preferences. We see this map as a first step in devising a value-based user model for music recommender systems.
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Poster
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- SPTime Slice Imputation for Personalized Goal-based Recommendation in Higher Education
by Weijie Jiang, Zachary A. Pardos
Learners are often faced with the following scenario: given a goal for the future, and what they have learned in the past, what should they do now to best achieve their goal? We build on work utilizing deep learning to make inferences about how past actions correspond to future outcomes and enhance this work with a novel application of backpropagation to learn per-user optimized actions. We apply this technique to two datasets, one from a university setting in which courses can be recommended towards preparation for a target course, and one from massive open online courses (MOOCs) in which course pages can be recommended towards quiz preparation. In both cases, our algorithm is applied to recommend actions the learner can take to maximize a desired future achievement objective, given their past actions and performance.
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- SPTraversing Semantically Annotated Queries for Task-oriented Query Recommendation
by Arthur Câmara, Rodrygo L. T. Santos
As search systems gradually turn into intelligent personal assistants, users increasingly resort to a search engine to accomplish a complex task, such as planning a trip, renting an apartment, or investing in stocks. A key challenge for the search engine is to understand the user’s underlying task given a sample query like ‘tickets to panama’, ‘studios in los angeles’, or ‘spotify stocks’, and to suggest other queries to help the user complete the task. In this paper, we investigate several strategies for query recommendation by traversing a semantically annotated query log using a mixture of explicit and latent representations of entire queries and of query segments. Our results demonstrate the effectiveness of these strategies in terms of utility and diversity, as well as their complementarity, with significant improvements compared to state-of-the-art query recommendation baselines adapted for this task.
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- SPOUser-Centered Evaluation of Strategies for Recommending Sequences of Points of Interest to Groups
by Daniel Herzog, Wolfgang Wörndl
Most recommender systems (RSs) predict the preferences of individual users; however, in certain scenarios, recommendations need to be made for a group of users. Tourism is a popular domain for group recommendations because people often travel in groups and look for point of interest (POI) sequences for their visits during a trip. In this study, we present different strategies that can be used to recommend POI sequences for groups. In addition, we introduce novel approaches, including a strategy called Split Group, which allows groups to split into smaller groups during a trip. We compared all strategies in a user study with 40 real groups. Our results proved that there was a significant difference in the quality of recommendations generated by using the different strategies. Most groups were willing to split temporarily during a trip, even when they were traveling with persons close to them. In this case, Split Group generated the best recommendations for different evaluation criteria. We use these findings to propose improvements for group recommendation strategies in the tourism domain.
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Paper Session 2: User Side of Recommender Systems
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- SPUser-Centric Evaluation of Session-Based Recommendations for an Automated Radio Station
by Malte Ludewig, Dietmar Jannach
The creation of an automated and virtually endless playlist given a start item is a common feature of modern media streaming services. When no past information about the user’s preferences is available, the creation of such playlists can be done using session-based recommendation techniques. In this case, the recommendations only depend on the start item and the user’s interactions in the current listening session, such as ‘liking’ or skipping an item. In recent years, various novel session-based techniques were proposed, often based on deep learning. The evaluation of such approaches is in most cases solely based on offline experimentation and abstract accuracy measures. However, such evaluations cannot inform us about the quality as perceived by users. To close this research gap, we have conducted a user study (N=250), where the participants interacted with an automated online radio station. Each treatment group received recommendations that were generated by one of five different algorithms. Our results show that comparably simple techniques led to quality perceptions that are similar or even better than when a complex deep learning mechanism or Spotify’s recommendations are used. The simple mechanisms, however, often tend to recommend comparably popular tracks, which can lead to lower discovery effects.
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