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- Ask the GRU: Multi-task Learning for Deep Text Recommendations
by Trapit Bansal, David Belanger, Andrew McCallum
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping. In this paper we present a method leveraging deep recurrent neural networks to encode the text sequence into a latent vector, specifically gated recurrent units (GRUs) trained end-to-end on the collaborative filtering task. For the task of scientific paper recommendation, this yields models with significantly higher accuracy. In cold-start scenarios, we beat the previous state-of-the-art, all of which ignore word order. Performance is further improved by multi-task learning, where the text encoder network is trained for a combination of content recommendation and item metadata prediction. This regularizes the collaborative filtering model, ameliorating the problem of sparsity of the observed rating matrix.
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Paper Session 3 – Cold Start and Hybrid Methods
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- Bayesian Low-Rank Determinantal Point Processes
by Mike Gartrell, Ulrich Paquet, Noam Koenigstein
Determinantal point processes (DPPs) are an emerging model for encoding probabilities over subsets, such as shopping baskets, selected from a ground set, such as an item catalog. They have recently proved to be appealing models for a number of machine learning tasks, including product recommendation. DPPs are parametrized by a positive semi-definite kernel matrix. Prior work has shown that using a low-rank factorization of this kernel provides scalability improvements that open the door to training on large-scale datasets and computing online recommendations, both of which are infeasible with standard DPP models that use a full-rank kernel. A low-rank DPP model can be trained using an optimization-based method, such as stochastic gradient ascent, to find a point estimate of the kernel parameters, which can be performed efficiently on large-scale datasets. However, this approach requires careful tuning of regularization parameters to prevent overfitting and provide good predictive performance, which can be computationally expensive. In this paper we present a Bayesian method for learning a low-rank factorization of this kernel, which provides automatic control of regularization. We show that our Bayesian low-rank DPP model can be trained efficiently using stochastic gradient Hamiltonian Monte Carlo (SGHMC). Our Bayesian model generally provides better predictive performance on several real-world product recommendation datasets than optimization-based low-rank DPP models trained using stochastic gradient ascent, and better performance than several state-of-the art recommendation methods in many cases.
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Paper Session 11 – Algorithms II
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- Convolutional Matrix Factorization for Document Context-Aware Recommendation
by Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyong Lee, Hwanjo Yu
Sparseness of user-to-item rating data is one of the major factors that deteriorate the quality of recommender system. To handle the sparsity problem, several recommendation techniques have been proposed that additionally consider auxiliary information to improve rating prediction accuracy. In particular, when rating data is sparse, document modeling-based approaches have improved the accuracy by additionally utilizing textual data such as reviews, abstracts, or synopses. However, due to the inherent limitation of the bag-of-words model, they have difficulties in effectively utilizing contextual information of the documents, which leads to shallow understanding of the documents. This paper proposes a novel context-aware recommendation model, convolutional matrix factorization (ConvMF) that integrates convolutional neural network (CNN) into probabilistic matrix factorization (PMF). Consequently, ConvMF captures contextual information of documents and further enhances the rating prediction accuracy. Our extensive evaluations on three real-world datasets show that ConvMF significantly outperforms the state-of-the-art recommendation models even when the rating data is extremely sparse. We also demonstrate that ConvMF successfully captures subtle contextual difference of a word in a document. Our implementation and datasets are available at http://dm.postech.ac.kr/ConvMF.
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Paper Session 8 – Deep Learning
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- Crowd-Based Personalized Natural Language Explanations for Recommendations
by Shuo Chang, F. Maxwell Harper, Loren Gilbert Terveen
Explanations are important for users to make decisions on whether to take recommendations. However, algorithm generated explanations can be overly simplistic and unconvincing. We believe that humans can overcome these limitations. Inspired by how people explain word-of-mouth recommendations, we designed a process, combining crowdsourcing and computation, that generates personalized natural language explanations. We modeled key topical aspects of movies, asked crowdworkers to write explanations based on quotes from online movie reviews, and personalized the explanations presented to users based on their rating history. We evaluated the explanations by surveying 220 MovieLens users, finding that compared to personalized tag-based explanations, natural language explanations: 1) contain a more appropriate amount of information, 2) earn more trust from users, and 3) make users more satisfied. This paper contributes to the research literature by describing a scalable process for generating high quality and personalized natural language explanations, improving on state-of-the-art content-based explanations, and showing the feasibility and advantages of approaches that combine human wisdom with algorithmic processes.
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Paper Session 5 – Trust and Reliability
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- Discovering What You’re Known For: A Contextual Poisson Factorization Approach
by Haokai Lu, James Caverlee, Wei Niu
Discovering what people are known for is valuable to many important applications such as recommender systems. Unlike an individual’s personal interests, what a user is known for is reflected by the views of others, and is often not easily discerned for a long-tail of the vast majority of users. In this paper, we tackle the problem of discovering what users are known for through a probabilistic model called Bayesian Contextual Poisson Factorization. Moving beyond just modeling user’s content, it naturally models and integrates additional contextual factors, concretely, user’s geo-spatial footprints and social influence, to overcome noisy online activities and social relations. Through GPS-tagged social media datasets, we find that the proposed method can improve known-for prediction performance by 17.5% in precision and 20.9% in recall on average, and that it can capture the implicit relationships between a user’s known-for profile and her content, geo-spatial and social influence.
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Paper Session 9 – Contextual Challenges
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- Domain-Aware Grade Prediction and Top-n Course Recommendation
by Asmaa Elbadrawy, George Karypis
Automated course recommendation can help deliver personalized and effective college advising and degree planning. Nearest neighbor and matrix factorization based collaborative filtering approaches have been applied to student-course grade data to help students select suitable courses. However, the student-course enrollment patterns exhibit grouping structures that are tied to the student and course academic features, which lead to grade data that are not missing at random (NMAR). Existing approaches for dealing with NMAR data, such as Response-aware and context-aware matrix factorization, do not model NMAR data in terms of the user and item features and are not designed with the characteristics of grade data in mind. In this work we investigate how the student and course academic features influence the enrollment patterns and we use these features to define student and course groups at various levels of granularity. We show how these groups can be used to design grade prediction and top-n course ranking models for neighborhood-based user collaborative filtering, matrix factorization and popularity-based ranking approaches. These methods give lower grade prediction error and more accurate top-n course rankings than the other methods that do not take domain knowledge into account.
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Paper Session 6 – Applications
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- Efficient Bayesian Methods for Graph-based Recommendation
by Ramon Lopes, Renato Assunção, Rodrygo L. T. Santos
Short-length random walks on the bipartite user-item graph have recently been shown to provide accurate and diverse recommendations. Nonetheless, these approaches suffer from severe time and space requirements, which can be alleviated via random walk sampling, at the cost of reduced recommendation quality. In addition, these approaches ignore users’ ratings, which further limits their expressiveness. In this paper, we introduce a computationally efficient graph-based approach for collaborative filtering based on short-path enumeration. Moreover, we propose three scoring functions based on the Bayesian paradigm that effectively exploit distributional aspects of the users’ ratings. We experiment with seven publicly available datasets against state-of-the-art graph-based and matrix factorization approaches. Our empirical results demonstrate the effectiveness of the proposed approach, with significant improvements in most settings. Furthermore, analytical results demonstrate its efficiency compared to other graph-based approaches.
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Paper Session 11 – Algorithms II
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- Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence
by Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei
Matrix factorization (MF) models and their extensions are standard in modern recommender systems. MF models decompose the observed user-item interaction matrix into user and item latent factors. In this paper, we propose a co-factorization model, CoFactor, which jointly decomposes the user-item interaction matrix and the item-item co-occurrence matrix with shared item latent factors. For each pair of items, the co-occurrence matrix encodes the number of users that have consumed both items. CoFactor is inspired by the recent success of word embedding models (e.g., word2vec) which can be interpreted as factorizing the word co-occurrence matrix. We show that this model significantly improves the performance over MF models on several datasets with little additional computational overhead. We provide qualitative results that explain how CoFactor improves the quality of the inferred factors and characterize the circumstances where it provides the most significant improvements.
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Paper Session 2 – Algorithms I
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- Field-aware Factorization Machines for CTR Prediction
by Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, Chih-Jen Lin
Click-through rate (CTR) prediction plays an important role in computational advertising. Models based on degree-2 polynomial mappings and factorization machines (FMs) are widely used for this task. Recently, a variant of FMs, field-aware factorization machines (FFMs), outperforms existing models in some world-wide CTR-prediction competitions. Based on our experiences in winning two of them, in this paper we establish FFMs as an effective method for classifying large sparse data including those from CTR prediction. First, we propose efficient implementations for training FFMs. Then we comprehensively analyze FFMs and compare this approach with competing models. Experiments show that FFMs are very useful for certain classification problems. Finally, we have released a package of FFMs for public use.
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Paper Session 2 – Algorithms I
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- Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks
by Evgeny Frolov, Ivan Oseledets
Conventional collaborative filtering techniques treat a top-n recommendations problem as a task of generating a list of the most relevant items. This formulation, however, disregards an opposite — avoiding recommendations with completely irrelevant items. Due to that bias, standard algorithms, as well as commonly used evaluation metrics, become insensitive to negative feedback. In order to resolve this problem we propose to treat user feedback as a categorical variable and model it with users and items in a ternary way. We employ a third-order tensor factorization technique and implement a higher order folding-in method to support online recommendations. The method is equally sensitive to entire spectrum of user ratings and is able to accurately predict relevant items even from a negative only feedback. Our method may partially eliminate the need for complicated rating elicitation process as it provides means for personalized recommendations from the very beginning of an interaction with a recommender system. We also propose a modification of standard metrics which helps to reveal unwanted biases and account for sensitivity to a negative feedback. Our model achieves state-of-the-art quality in standard recommendation tasks while significantly outperforming other methods in the cold-start “no-positive-feedback” scenarios.
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Paper Session 3 – Cold Start and Hybrid Methods
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- Gaze Prediction for Recommender Systems
by Qian Zhao, Shuo Chang, F. Maxwell Harper, Joseph A. Konstan
As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users’ preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens — an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).
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Paper Session 4 – User in the Loop
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- Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks
by Roy Levin, Hassan Abassi, Uzi Cohen
Online social networks have become predominant in recent years and have grown to encompass massive scales of data. In addition to data scale, these networks can be heterogeneous and contain complex structures between different users, between social entities and various interactions between users and social entities. This is especially true in enterprise social networks where hierarchies explicitly exist between employees as well. In such networks, producing the best recommendations for each user is a very challenging problem for two main reasons. First, the complex structures in the social network need to be properly mined and exploited by the algorithm. Second, these networks contain millions or even billions of edges making the problem very difficult computationally. In this paper we present Guided Walk, a supervised graph based algorithm that learns the significance of different network links for each user and then produces entity recommendations based on this learning phase. We compare the algorithm with a set of baseline algorithms using offline evaluation techniques as well as a user survey. The offline results show that the algorithm outperforms the next best algorithm by a factor of 3.6. The user survey further confirms that the recommendation are not only relevant but also rank high in terms of personal relevance for each user. To deal with large scale social networks, the Guided Walk algorithm is formulated as a Pregel program which allows us to utilize the power of distributed parallel computing. This would allow horizontally scaling the algorithm for larger social networks by simply adding more compute nodes to the cluster.
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Paper Session 10 – Social Perspective
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- Joint User Modeling across Aligned Heterogeneous Sites
by Xuezhi Cao, Yong Yu
An accurate and comprehensive user modeling technique is crucial for the quality of recommender systems. Traditionally, we model user preferences using only actions from the target site and may suffer from cold-start problem. As nowadays people normally engage in multiple online sites for various needs, we consider leveraging the cross-site actions to improve the user modeling accuracy. Specifically, in this paper we aim at achieving a more comprehensive and accurate user modeling by modeling user’s actions in multiple aligned heterogeneous sites simultaneously. To do so, we propose a modularized probabilistic graphical model framework JUMA. We further integrate topic model and matrix factorization into JUMA for joint user modeling over text-based and item-based sites. We assemble and publish large-scale dataset for comprehensive analyzing and evaluation. Experimental results show that our framework JUMA out performs traditional within-site user modeling techniques, especially for cold-start scenarios. For cold-start users, we achieve relative improvements of 9.3% and 12.8% comparing to existing within-site approaches for recommendation in item-based and text-based sites respectively. Thus we draw the conclusion that aligning heterogeneous sites and modeling users jointly do help to improve the quality of online recommender systems.
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Paper Session 3 – Cold Start and Hybrid Methods
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- Latent Factor Representations for Cold-Start Video Recommendation
by Sujoy Roy, Sharat Chandra Guntuku
Recommending items that have rarely/never been viewed by users is a bottleneck for collaborative filtering (CF) based recommendation algorithms. To alleviate this problem, item content representation (mostly in textual form) has been used as auxiliary information for learning latent factor representations. In this work we present a novel method for learning latent factor representation for videos based on modelling the emotional connection between user and item. First of all we present a comparative analysis of state-of-the art emotion modelling approaches that brings out a surprising finding regarding the efficacy of latent factor representations in modelling emotion in video content. Based on this finding we present a method visual-CLiMF for learning latent factor representations for cold start videos based on implicit feedback. Visual-CLiMF is based on the popular collaborative less-is-more approach but demonstrates how emotional aspects of items could be used as auxiliary information to improve MRR performance. Experiments on a new data set and the Amazon products data set demonstrate the effectiveness of visual-CLiMF which outperforms existing CF methods with or without content information.
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Paper Session 3 – Cold Start and Hybrid Methods
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- Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization
by Jie Yang, Zhu Sun, Alessandro Bozzon, Jie Zhang
Existing feature-based recommendation methods incorporate auxiliary features about users and/or items to address data sparsity and cold start issues. They mainly consider features that are organized in a flat structure, where features are independent and in a same level. However, auxiliary features are often organized in rich knowledge structures (e.g. hierarchy) to describe their relationships. In this paper, we propose a novel matrix factorization framework with recursive regularization — ReMF, which jointly models and learns the influence of hierarchically-organized features on user-item interactions, thus to improve recommendation accuracy. It also provides characterization of how different features in the hierarchy co-influence the modeling of user-item interactions. Empirical results on real-world data sets demonstrate that ReMF consistently outperforms state-of-the-art feature-based recommendation methods.
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Paper Session 2 – Algorithms I
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- BPNLocal Item-Item Models For Top-N Recommendation
by Evangelia Christakopoulou, George Karypis
Item-based approaches based on SLIM (Sparse LInear Methods) have demonstrated very good performance for top-N recommendation; however they only estimate a single model for all the users. This work is based on the intuition that not all users behave in the same way — instead there exist subsets of like-minded users. By using different item-item models for these user subsets, we can capture differences in their preferences and this can lead to improved performance for top-N recommendations. In this work, we extend SLIM by combining global and local SLIM models. We present a method that computes the prediction scores as a user-specific combination of the predictions derived by a global and local item-item models. We present an approach in which the global model, the local models, their user-specific combination, and the assignment of users to the local models are jointly optimized to improve the top-N recommendation performance. Our experiments show that the proposed method improves upon the standard SLIM model and outperforms competing top-N recommendation approaches.
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Paper Session 2 – Algorithms I
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- Mechanism Design for Personalized Recommender Systems
by Qingpeng Cai, Aris Filos-Ratsikas, Chang Liu, Pingzhong Tang
Strategic behaviour from sellers on e-commerce websites, such as faking transactions and manipulating the recommendation scores through artificial reviews, have been among the most notorious obstacles that prevent websites from maximizing the efficiency of their recommendations. Previous approaches have focused almost exclusively on machine learning-related techniques to detect and penalize such behaviour. In this paper, we tackle the problem from a different perspective, using the approach of the field of mechanism design. We put forward a game model tailored for the setting at hand and aim to construct truthful mechanisms, i.e. mechanisms that do not provide incentives for dishonest reputation-augmenting actions, that guarantee good recommendations in the worst-case. For the setting with two agents, we propose a truthful mechanism that is optimal in terms of social efficiency. For the general case of m agents, we prove both lower and upper bound results on the effciency of truthful mechanisms and propose truthful mechanisms that yield significantly better results, when compared to an existing mechanism from a leading e-commerce site on real data.
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Paper Session 5 – Trust and Reliability
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- Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing
by Jermaine Marshall, Dong Wang
This work is motivated by the need to provide reliable information recommendation to users in social sensing. Social sensing has become an emerging application paradigm that uses humans as sensors to observe and report events in the physical world. These human sensed observations are often viewed as binary claims (either true or false). A fundamental challenge in social sensing is how to ascertain the credibility of claims and the reliability of sources without knowing either of them a priori. We refer to this challenge as truth discovery. While prior works have made progress on addressing this challenge, an important limitation exists: they did not explore the mood sensitivity aspect of the problem. Therefore, the claims identified as correct by current solutions can be completely biased in regards to the mood of human sources and lead to useless or even misleading recommendations. In this paper, we present a new analytical model that explicitly considers the mood sensitivity feature in the solution of truth discovery problem. The new model solves a multi-dimensional estimation problem to jointly estimate the correctness and mood neutrality of claims as well as the reliability and mood sensitivity of sources. We compare our model with state-of-the-art truth discovery solutions using four real world datasets collected from Twitter during recent disastrous and emergent events: Brussels Bombing, Paris Attack, Oregon Shooting, Baltimore Riots, which occurred in 2015 and 2016. The results show that our model has significant improvements over the compared baselines by finding more correct and mood neutral claims.
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Paper Session 5 – Trust and Reliability
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- Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
by Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk
Real-life recommender systems often face the daunting task of providing recommendations based only on the clicks of a user session. Methods that rely on user profiles — such as matrix factorization — perform very poorly in this setting, thus item-to-item recommendations are used most of the time. However the items typically have rich feature representations such as pictures and text descriptions that can be used to model the sessions. Here we investigate how these features can be exploited in Recurrent Neural Network based session models using deep learning. We show that obvious approaches do not leverage these data sources. We thus introduce a number of parallel RNN (p-RNN) architectures to model sessions based on the clicks and the features (images and text) of the clicked items. We also propose alternative training strategies for p-RNNs that suit them better than standard training. We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline.
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Paper Session 8 – Deep Learning
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- Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach
by Rose Catherine, William Cohen
Improving the performance of recommender systems using knowledge graphs is an important task. There have been many hybrid systems proposed in the past that use a mix of content-based and collaborative filtering techniques to boost the performance. More recently, some work has focused on recommendations that use external knowledge graphs (KGs) to supplement content-based recommendation. In this paper, we investigate three methods for making KG based recommendations using a general-purpose probabilistic logic system called ProPPR. The simplest of the models, EntitySim, uses only the links of the graph. We then extend the model to TypeSim that also uses the types of the entities to boost its generalization capabilities. Next, we develop a graph based latent factor model, GraphLF, which combines the strengths of latent factorization with graphs. We compare our approaches to a recently proposed state-of-the-art graph recommendation method on two large datasets, Yelp and MovieLens-100K. The experiments illustrate that our approaches can give large performance improvements. Additionally, we demonstrate that knowledge graphs give maximum advantage when the dataset is sparse, and gradually become redundant as more training data becomes available, and hence are most useful in cold-start settings.
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Paper Session 11 – Algorithms II
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- Recommending New Items to Ephemeral Groups Using Contextual User Influence
by Elisa Quintarelli, Emanuele Rabosio, Letizia Tanca
Group recommender systems help groups of users in finding appropriate items to be enjoyed together. Lots of activities, like watching TV or going to the restaurant, are intrinsically group-based, thus making the group recommendation problem very relevant. In this paper we study ephemeral groups, i.e., groups where the members might be together for the first time. Recent approaches have tackled this issue introducing complex models to be learned offline, making them unable to deal with new items; on the contrary, we propose a group recommender able to manage new items too. In more detail, our technique determines the preference of a group for an item by combining the individual preferences of the group members on the basis of their contextual influence, where the contextual influence represents the ability of an individual, in a given situation, to direct the group’s decision. We conducted an extensive experimental evaluation on a TV dataset containing a log of viewings performed by real groups, showing how our approach outperforms the comparable techniques from the literature.
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Paper Session 10 – Social Perspective
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- Representation Learning for Homophilic Preferences
by Trong T. Nguyen, Hady W. Lauw
Users express their personal preferences through ratings, adoptions, and other consumption behaviors. We seek to learn latent representations for user preferences from such behavioral data. One representation learning model that has been shown to be effective for large preference datasets is Restricted Boltzmann Machine (RBM). While homophily, or the tendency of friends to share their preferences at some level, is an established notion in sociology, thus far it has not yet been clearly demonstrated on RBM-based preference models. The question lies in how to appropriately incorporate social network into the architecture of RBM-based models for learning representations of preferences. In this paper, we propose two potential architectures: one that models social network among users as additional observations, and another that incorporates social network into the sharing of hidden units among related users. We study the efficacies of these proposed architectures on publicly available, real-life preference datasets with social networks, yielding useful insights.
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Paper Session 10 – Social Perspective
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- STAR: Semiring Trust Inference for Trust-Aware Social Recommenders
by Peixin Gao, Hui Miao, John S Baras, Jennifer Golbeck
Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.
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Paper Session 10 – Social Perspective
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- BPNTAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation
by Hancheng Ge, James Caverlee, Haokai Lu
We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being “favorited” many times), there is a long-tail of potential candidates for whom we have little evidence. Through careful modeling of contextual factors like the geo-spatial, topical, and social preferences of users, we propose a tensor-based personalized expert recommendation framework that integrates these factors for revealing latent connections between homogeneous entities (e.g., users and users) and between heterogeneous entities (e.g., users and experts). Through extensive experiments over geo-tagged Twitter data, we find that the proposed framework can improve the quality of recommendation by over 30% in both precision and recall compared to the state-of-the-art.
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Paper Session 9 – Contextual Challenges
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- Using Navigation to Improve Recommendations in Real-Time
by Chao-Yuan Wu, Christopher V Alvino, Alexander J Smola, Justin Basilico
Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the opportunity of immediate personalization for a user while the user is navigating recommendations. We propose a novel strategy to address the above problem in a principled manner. The key insight is that as we observe a user’s interactions, it reveals much more information about her desires. We exploit this by inferring the within-session user intent on-the-fly based on navigation interactions, since they offer valuable clues into a user’s current state of mind. Using navigation patterns and adapting recommendations in real-time creates an opportunity to provide more accurate recommendations. By prefetching a larger amount of content, this can be carried out entirely in the client (such as a browser) without added latency. We define a new Bayesian model with an efficient inference algorithm. We demonstrate significant improvements with this novel approach on a real-world, large-scale dataset from Netflix on the problem of adapting the recommendations on a user’s homepage.
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Paper Session 11 – Algorithms II
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- Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation
by Ruining He, Chen Fang, Zhaowen Wang, Julian McAuley
Understanding users’ interactions with highly subjective content—like artistic images—is challenging due to the complex semantics that guide our preferences. On the one hand one has to overcome `standard’ recommender systems challenges, such as dealing with large, sparse, and long-tailed datasets. On the other, several new challenges present themselves, such as the need to model content in terms of its visual appearance, or even social dynamics, such as a preference toward a particular artist that is independent of the art they create. In this paper we build large-scale recommender systems to model the dynamics of a vibrant digital art community, Behance, consisting of tens of millions of interactions (clicks and `appreciates’) of users toward digital art. Methodologically, our main contributions are to model (a) rich content, especially in terms of its visual appearance; (b) temporal dynamics, in terms of how users prefer `visually consistent’ content within and across sessions; and (c) social dynamics, in terms of how users exhibit preferences both towards certain art styles, as well as the artists themselves.
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Paper Session 10 – Social Perspective
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