Short Paper and Demo Slam
Date: Wednesday, Sept 16, 2015, 17:30-18:30
Location: HS 1
Authors of short papers and demos explain in 2 minute elevator pitches the main idea of their contribution and why participants should come and see their poster/demo.
Posters and demos are then exhibited during the reception. Afterwards, posters are exhibited in the designated poster area.
List of all short papers accepted for RecSys 2015 (in alphabetical order)
- A Study of Priors for Relevance-Based Language Modelling of Recommender Systems
by Daniel Valcarce, Javier Parapar and Álvaro Barreiro
Probabilistic modelling of recommender systems naturally introduces the concept of prior probability into the recommendation task. Relevance-Based Language Models, a principled probabilistic query expansion technique in Information Retrieval, has been recently adapted to the item recommendation task with success. In this paper, we study the effect of the item and user prior probabilities under that framework. We adapt two priors from the document retrieval field and then we propose other two new probabilistic priors. Evidence gathered from experimentation indicates that a linear prior for the neighbour and a probabilistic prior based on Dirichlet smoothing for the items improve the quality of the item recommendation ranking.
- Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models
by Mehdi Hosseinzadeh Aghdam, Negar Hariri, Bamshad Mobasher and Robin Burke
Recommender systems help users find items of interest by tailoring their recommendations to users’ personal preferences. The utility of an item for a user, however, may vary greatly depending on that user’s specific situation or the context in which the item is used. Without considering these changes in preferences, the recommendations may match the general preferences of a user, but they may have small value for the user in his/her current situation. In this paper, we introduce a hierarchical hidden Markov model for capturing changes in user’s preferences. Using a user’s feedback sequence on items, we model the user as a hierarchical hidden Markov process and the current context of the user as a hidden variable in this model. For a given user, our model is used to infer the maximum likelihood sequence of transitions between contextual states and to predict the probability distribution for the context of the next action. The predicted context is then used to generate recommendations. Our evaluation results using Last.fm music playlist data, indicate that this approach achieves significantly better performance in terms of accuracy and diversity compared to baseline methods.
- Are Real-World Place Recommender Algorithms Useful in Virtual World Environments?
by Leandro Balby Marinho, Christoph Trattner and Denis Parra
Large scale virtual worlds such as massive multiplayer online games or 3D worlds gained tremendous popularity over the past few years. With the large and ever increasing amount of content available, virtual world users face the information overload problem. To tackle this issue, game-designers usually deploy recommendation services with the aim of making the virtual world a more joyful environment to be connected at. In this context, we present in this paper the results of a project that aims at understanding the mobility patterns of virtual world users in order to derive place recommenders for helping them to explore content more efficiently. Our study focus on the virtual world SecondLife, one of the largest and most prominent in recent years. Since SecondLife is comparable to real-world Location-based Social Networks (LBSNs), i.e., users can both check-in and share visited virtual places, a natural approach is to assume that place recommenders that are known to work well on real-world LBSNs will also work well on SecondLife. We have put this assumption to the test and found out that (i) while collaborative filtering algorithms have compatible performances in both environments, (ii) existing place recommenders based on geographic metadata are not useful in SecondLife.
- Asymmetric Recommendations: The Interacting Effects of Social Ratings’ Direction and Strength on Users’ Ratings
by Oded Nov and Ofer Arazy
In social recommendation systems, users often publicly rate objects such as photos, news articles or consumer products. When they appear in aggregate, these ratings carry social signals such as the direction and strength of the raters’ average opinion about the product. Using a controlled experiment we manipulated two central social signals – the direction and strength of social ratings of five popular consumer products – and examined their interacting effects on users’ ratings. The results show an asymmetric user behavior, where the direction of perceived social rating has a negative effect on users’ ratings if the direction of perceived social rating is negative, but no effect if the direction is positive. The strength of perceived social ratings did not have a significant effect on users’ ratings. The findings highlight the potential for cascading adverse effects of small number of negative user ratings on subsequent users’ opinions.
- Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates
by Elizabeth M. Daly, Michele Berlingerio and François Schnitzler
Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying articipants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.
- Data Quality Matters in Recommender Systems
by Oren Sar Shalom, Shlomo Berkovsky, Royi Ronen, Elad Ziklik and Amir Amihood
Although data quality has been recognized as an important factor in the broad information systems research, it has received little attention in recommender systems. Data quality matters are typically addressed in recommenders by ad-hoc cleansing methods, which prune noisy or unreliable records from the data. However, the setting of the cleansing parameters is often done arbitrarily, without thorough consideration of the data characteristics. In this work, we turn to two central data quality problems in recommender systems: sparsity and redundancy. We devise models for setting data-dependent thresholds and sampling levels, and evaluate these using a collection of public and proprietary datasets. We observe that the models accurately predict data cleansing parameters, while having minor effect on the accuracy of the generated recommendations.
- Elsevier Journal Finder: Recommending Journals for your Paper
by Ning Kang, Marius Doornenbal and Bob Schijvenaars
Rejection is the norm in academic publishing. One of the main reasons for rejections is that the topics of the submitted papers are not relevant to the scope of the journal, even when the papers themselves are excellent. Submission to a journal that fits well with the publication may avoid this issue. A system that is able to suggest journals that have published similar articles to the submitted papers may help authors choose where to submit. The Elsevier journal finder, a freely available online service, is one of the most comprehensive journal recommender systems, covering all scientific domains and more than 2,900 per-reviewed Elsevier journals. The system uses natural language processing for feature generation, and Okapi BM25 matching for the recommendation algorithm. The procedure is to paste text, such as an abstract, and get a list of recommend journals and relevant metadata. The website URL is http://journalfinder.elsevier.com.
- Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study
by Dominik Kowald and Elisabeth Lex
To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches. We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems.
- Good Times Bad Times: A Study on Recency Effects in Collaborative Filtering for Social Tagging
by Santiago Larrain, Christoph Trattner, Denis Parra, Eduardo Graells-Garrido and Kjetil Nørvåg
In this paper, we present work-in-progress of a recently started project that aims at studying the effect of time in recommender systems in the context of social tagging. Despite the existence of previous work in this area, no research has yet made an extensive evaluation and comparison of time-aware recommendation methods. With this motivation, this paper presents results of a study where we focused on understanding (i) “when” to use the temporal information into traditional collaborative filtering (CF) algorithms, and (ii) “how” to weight the similarity between users and items by exploring the effect of different time-decay functions. As the results of our extensive evaluation conducted over five social tagging systems (Delicious, BibSonomy, CiteULike, MovieLens, and Last.fm) suggest, the step (when) in which time is incorporated in the CF algorithm has substantial effect on accuracy, and the type of time-decay function (how) plays a role on accuracy and coverage mostly under pre-filtering on user-based CF, while item-based shows stronger stability over the experimental conditions.
- Improving the User Experience during Cold Start through Choice-Based Preference Elicitation
by Mark P. Graus and Martijn C. Willemsen
We studied an alternative choice-based interface for preference elicitation during the cold start phase and compared it directly with a standard rating-based interface. In this alternative interface users started from a diverse set covering all movies and iteratively narrowed down through a matrix factorization latent feature space to smaller sets of items based on their choices. The results show that compared to a rating-based interface, the choice-based interface requires less effort and results in more satisfying recommendations, showing that it might be a promising candidate for alleviating the cold start problem of new users.
- Incremental Matrix Factorization via Feature Space Re-learning for Recommender System
by Qiang Song, Jian Cheng and Hanqing Lu
Matrix factorization is widely used in Recommender Systems. Although existing popular incremental matrix factorization methods are effectively in reducing time complexity, they simply assume that the similarity between items or users is invariant. For instance, they keep the item feature matrix unchanged and just update the user matrix without re-training the entire model. However, with the new users growing continuously, the fitting error would be accumulated since the extra distribution information of items has not been utilized. In this paper, we present an alternative and reasonable approach, with a relaxed assumption that the similarity between items (users) is relatively stable after updating. Concretely, utilizing the prediction error of the new data as the auxiliary features, our method updates both feature matrices simultaneously, and thus users’ preference can be better modeled than merely adjusting one corresponded feature matrix. Besides, our method maintains the feature dimension in a smaller size through taking advantage of matrix sketching. Experimental results show that our proposal outperforms the existing incremental matrix factorization methods.
- Latent Trajectory Modeling: A Light and Efficient Way to Introduce Time in Recommender Systems
by Elie Guàrdia-Sebaoun, Vincent Guigue and Patrick Gallinari
For recommender systems, time is often an important source of information but it is also a complex dimension to apprehend. We propose here to learn item and user representations such that any timely ordered sequence of items selected by a user will be represented as a trajectory of the user in a representation space. This allows us to rank new items for this user. We then enrich the item and user representations in order to perform rating prediction using a classical matrix factorization scheme. We demonstrate the interest of our approach regarding both item ranking and rating prediction on a series of classical benchmarks.
- Making the Most of Preference Feedback by Modeling Feature Dependencies
by S Chandra Mouli and Sutanu Chakraborty
Conversational recommender systems help users navigate through the product space by exploiting feedback. In conversational systems based on preference-based feedback, the user selects the most preferred item from a list of recommended products. Modelling user’s preferences then becomes important in order to recommend relevant items. Several existing recommender systems accomplish this by assuming the features to be independent. Here we will attempt to forego this assumption and exploit the dependencies between the features to build a robust user preference model.
- Nudging Grocery Shoppers to Make Healthier Choices
by Elizabeth Wayman and Sriganesh Madhvanath
Despite the rampant increase in obesity rates and concomitant increases in rates of mortality from heart disease, cancer and diabetes, getting the general public to adopt a healthy diet has proven to be challenging for a variety of reasons. In this paper, we describe Foodle, a research project aimed at providing automated, personalized and goal-driven dietary guidance to users based on their grocery receipt data, by leveraging the availability of digital receipts for grocery store purchases. We discuss challenges faced, the current state of the project, and directions for future work.
- Nuke `Em Till They Go: Investigating Power User Attacks to Disparage Items in Collaborative Recommenders
by Carlos E. Seminario and David C. Wilson
Recommender Systems (RSs) can be vulnerable to manipulation by malicious users who successfully bias recommendations for their own benefit or pleasure. These are known as attacks on RSs and are typically used to either promote (“push”) or disparage (“nuke”) targeted items contained within the recommender’s user-item dataset. Our recent work with the Power User Attack (PUA) attack model, determined that attackers disguised as influential power users can mount successful (from the attacker’s viewpoint) push attacks against user-based, item-based, and SVD-based recommenders. However, the success of push attack vectors may not be symmetric for nuke attacks, which target the opposite effect — reducing the likelihood that target items appear in users’ top-N lists. The asymmetry between push and nuke attacks is highlighted when evaluating these attacks using traditional robustness metrics such as Rank and Prediction Shift. This paper examines the PUA attack model in the context of nuke attacks, in order to investigate the differences between push and nuke attack orientations, as well as how they are evaluated. In this work we show that the PUA is able to mount successful nuke attacks against commonly-used recommender algorithms highlighting the “nuke vs. push” asymmetry in the results.
- ‘Please, Not Now!’ A Model for Timing Recommendations
by Nofar Dali Betzalel, Bracha Shapira and Lior Rokach
Proactive recommender systems push recommendations to users without their explicit request whenever a recommendation that suits a user is available. These systems strive to optimize the match between recommended items and users’ preferences. We assume that recommendations might be reflected with low accuracy not only due to the recommended items’ suitability to the user, but also because of the recommendations’ timings. We therefore claim that it is possible to learn a model of good and bad contexts for recommendations that can later be integrated in a recommender system. Using mobile data collected during a three weeks user study we suggest a two-phase model that is able to classify whether a certain context is at all suitable for any recommendation, regardless of its content. Results reveal that a hybrid model that first decides whether it should use a personal or a non-personal timing model, and then classifies accordingly whether the timing is proper for recommendations, is superior to both the personal or non-personal timing models.
- POI Recommendation: Towards Fused Matrix Factorization with Geographical and Temporal Influences
by Jean-Benoît Griesner, Talel Abdessalem and Hubert Naacke
Providing personalized point-of-interest (POI) recommendation has become a major issue with the rapid emergence of location-based social networks (LBSNs). Unlike traditional recommendation approaches, the LBSNs application domain comes with significant geographical and temporal dimensions. Moreover most of traditional recommendation algorithms fail to cope with the specific challenges implied by these two dimensions. Fusing geographical and temporal influences for better recommendation accuracy in LBSNs remains unexplored, as far as we know. We depict how matrix factorization can serve POI recommendation, and propose a novel attempt to integrate both geographical and temporal influences into matrix factorization. Specifically we present GeoMF-TD, an extension of geographical matrix factorization with temporal dependencies. Our experiments on a real dataset shows up to 20% benefit on recommendation precision.
- The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data
by Sam Banks, Rachael Rafter and Barry Smyth
This paper describes a casual Facebook game to capture recommendation data as a side-effect of gameplay. We show how this data can be used to make successful recommendations as part of a live-user trial.
- Top-N Recommendation with Missing Implicit Feedback
by Daryl Lim, Julian McAuley and Gert Lanckriet
In implicit feedback datasets, non-interaction of a user with an item does not necessarily indicate that an item is irrelevant for the user. Thus, evaluation measures computed on the observed feedback may not accurately reflect performance on the complete data. In this paper, we discuss a missing data model for implicit feedback and propose a novel evaluation measure oriented towards Top-N recommendation. Our evaluation measure admits unbiased estimation under our missing data model, unlike the popular Normalized Discounted Cumulative Gain (NDCG) measure. We also derive an efficient algorithm to optimize the measure on the training data. We run several experiments which demonstrate the utility of our proposed measure.
- Towards Automatic Meal Plan Recommendations for Balanced Nutrition
by David Elsweiler and Morgan Harvey
Food recommenders have been touted as a useful tool to help people achieve a healthy diet. Here we incorporate nutrition into the recommender problem by examining the feasibility of algorithmically creating daily meal plans for a sample of user profiles (n=100), combined with a diverse set of food preference data (n=64) collected in a natural setting. Our analyses demonstrate it is possible to recommend plans for a large percentage of users which meet the guidelines set out by international health agencies.
- Uncovering Systematic Bias in Ratings across Categories: a Bayesian Approach
by Fangjian Guo and David B. Dunson
Recommender systems are routinely equipped with standardized taxonomy that associates each item with one or more categories or genres. Although such information does not directly imply the quality of an item, the distribution of ratings vary greatly across categories, e.g. animation movies may generally receive higher ratings than action movies. While it is a natural outcome given the diversity and heterogeneity of both users and items, it makes directly aggregated ratings, which are commonly used to guide users’ choice by reflecting the overall quality of an item, incomparable across categories and hence prone to fairness and diversity issues. This paper aims to uncover and calibrate systematic category-wise biases for discrete-valued ratings. We propose a novel Bayesian multiplicative probit model that treats the inflation or deflation of mean rating for a combination of categories as multiplicatively contributed from category-specific parameters. The posterior distribution of those parameters, as inferred from data, can capture the bias for all possible combinations of categories, thus enabling statistically efficient estimation and principled rating calibration.
- User Churn Migration Analysis with DEDICOM
by Rafet Sifa, César Ojeda and Christian Bauckhage
Time plays an important role regarding user preferences for products. It introduces asymmetries into the adoption of products which should be considered the context of recommender systems and business intelligence. We therefore investigate how temporally asymmetric user preferences can be analyzed using a latent factor model called Decomposition Into Directional Components (DEDICOM). We introduce a new scalable hybrid algorithm that combines projected gradient descent and alternating least squares updates to compute DEDICOM and imposes semi-nonnegativity constraints to better interpret the resulting factors. We apply our model to analyze user churn and migration between different computer games in a social gaming environment.