Session 2: Novel Setups

Date: Tuesday, Oct 7, 14:00-15:45
Moderator: Domonkos Tikk

• Factored MDPs for Detecting Topics of User Sessions

  by Maryam Tavakol and Ulf Brefeld

  Recommender systems aim to capture interests of users to provide tailored recommendations. User interests are however often unique and depend on many unobservable factors including a user’s mood and the local weather. We take a contextual session-based approach and propose a sequential framework using factored Markov decision processes (fMDPs) to detect the user’s goal (the topic) of a session. We show that an independence assumption on the attributes of items leads to a set of independent models that can be optimised efficiently. Our approach results in interpretable topics that can be effectively turned into recommendations. Empirical results on a real world click log from a large e-commerce company exhibit highly accurate topic prediction rates of about 90%. Translating our approach into a topic-driven recommender system outperforms collaborative filtering methods by one order of magnitude.

• Context Adaptation in Interactive Recommender Systems

  by Negar Hariri, Bamshad Mobasher and Robin Burke

  Contextual factors can greatly influence the utility of recommendations for users. In many recommendation and personalization applications, particularly in domains where user context changes dynamically, it is difficult to represent and model contextual factors directly, but it is often possible to observe their impact on user preferences during the course of users’ interactions with the system. In this paper, we introduce an interactive recommender system that can detect and adapt to changes in context based on the user’s ongoing behavior. The system, then, dynamically tailors its recommendations to match the user’s most recent preferences. We formulate this problem as a multi-armed bandit problem and use Thompson sampling heuristic to learn a model for the user. Following the Thompson sampling approach, the user model is updated after each interaction as the system observes the corresponding rewards for the recommendations provided during that interaction. To generate contextual recommendations, the user’s preference model is monitored for changes at each step of interaction with the user and is updated incrementally. We will introduce a mechanism for detecting significant changes in the user’s preferences and will describe how it can be used to improve the performance of the recommender system.

• Question Recommendation with Constraints for Massive Open Online Courses

  by Diyi Yang, David Adamson and Carolyn Rose

  Massive Open Online Courses (MOOCs) have experienced a recent boom in interest. Although the number of students that have registered for MOOCs is remarkably high, the fraction of those who actively participate in course discussion forums is startlingly low. By recommending relevant forum discussions and questions to students, their engagement and participation may increase, to the benefit of both the student and the course community. This problem has not been thoroughly explored by existing recommender systems. In contrast to traditional product recommendation, question recommendation in discussion forums should consider constraints on both students and questions. These considerations include (1)Load Balancing – students should not be over-burdened with too many requests; and (2) Expertise Matching – matching students’ abilities to the difficulty of unanswered questions, which in turn positions students to contribute meaningfully to the forum. In this work, we propose a novel constrained question recommendation problem to address the above considerations, with the intent to improve the learning experience for course participants. We first design a context-aware matrix factorization model to predict students’ preferences over questions, then build a max cost flow model to address the constraints. Experimental results on three MOOC datasets demonstrate that our method significantly outperforms baseline methods in optimizing overall forum welfare, and in predicting which questions students might be interested in.

• Attacking Item-Based Recommender Systems with Power Items

  by Carlos Seminario and David Wilson

  Recommender Systems (RS) are vulnerable to attack by malicious users who intend to bias the recommendations for their own benefit. Research in this area has developed attack models, detection methods, and mitigation schemes to understand and protect against such attacks. For Collaborative Filtering RSs, model-based approaches such as item-based and matrix-factorization were found to be more robust to many types of attack. Advice in designing for system robustness has thus been to employ model-based approaches. Our recent work with the Power User Attack (PUA), however, determined that attackers disguised as influential users can successfully attack (from the attacker’s viewpoint) SVD-based recommenders, as well as user-based. Though item-based systems remained robust to the PUA. In this paper we investigate a new, complementary attack model, the Power Item Attack (PIA), that uses influential items to successfully attack RSs. We show that the PIA is able to impact not only user-based and SVD-based recommenders but also the heretofore highly robust item-based approach, using a novel multi-target attack vector.

• Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization

  by Smriti Bhagat, Udi Weinsberg, Stratis Ioannidis and Nina Taft

  Recommender systems leverage user demographic information, such as age, gender, etc., to personalize recommendations and better place their targeted ads. Oftentimes, users do not volunteer this information due to privacy concerns, or due to a lack of initiative in filling out their online profiles. We illustrate a new threat in which a recommender learns private attributes of users who do not voluntarily disclose them. We design both passive and active attacks that so- licit ratings for strategically selected items, and could thus be used by a recommender system to pursue this hidden agenda. Our methods are based on a novel usage of Bayesian matrix factorization in an active learning setting. Evaluations on multiple datasets illustrate that such attacks are indeed feasible and use significantly fewer rated items than static inference methods. Importantly, they succeed without sacrificing the quality of recommendations to users.

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