RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin

Reducing Offline Evaluation Bias in Recommendation Systems

Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty of evaluating a recommendation algorithm with historical data (via offline evaluation). This paper analyses this evaluation bias and proposes a simple item weighting solution that reduces its impact. The efficiency of the proposed solution is evaluated on real world data extracted from Viadeo professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

\u27Friends Group\u27 in Recommender Systems: Effects of User Involvement in the Formation of Recommending Groups

How can we improve the acceptance of recommendations in collaborative systems? The group identity of recommenders and recipient involvement in group formation impacts on the likelihood that users of social collaborative systems would accept recommendations provided on it. We introduce the term \u27friends group\u27 to describe a sub-group of the \u27neighbors group\u27 in recommender systems that is not solely rank-dependent, as opposed to \u27neighbors\u27 that are assigned by rating similarity. The \u27friends group\u27 is unique because of the user\u27s involvement in its formation and the user\u27s ability to choose the characteristics of its members. The latter aspect corresponds to Festinger\u27s Social Comparison Theory , suggesting that \u27neighbors\u27 (like-minded groups) are relevant for \u27low-risk\u27 domains whereas similarity-based \u27friends\u27 are more relevant for \u27high-risk\u27 domains. We conducted a two year field study, using QSIA, a Web-based Java-programmed collaborative system for collection, management, sharing and assignment of learning knowledge items. QSIA was implemented in over ten courses in several universities. QSIA database and logs contained approximately 31,000 records of items-seeking acts, 3,000 users, 10,000 items, 3,000 rankings and knowledge items from 30 domains. We found that the difference between acceptance and rejection ratios of recommendations when the items originated from an advising group comprised of \u27friends\u27, is significantly higher than when the advising group is the more commonly known \u27neighbors group\u27. The difference increases for frequently recommended as opposed to other items and for experienced as opposed to \u27average\u27 users. Our longitudinal analysis indicates a positive learning curve for experienced users, who, over time, increasingly preferred \u27friends group\u27 over \u27neighbors group\u27 as their experience with the system increases. Also, users chose their own group to participate in the advising group significantly more than other groups. The contribution of this study is in explicating the relationship between the perceived quality of the recommendation (measured in terms of usage actions ), and the user\u27s involvement in the formation of the advising group. The major implication of our findings for the development of recommender systems is the need to enhance involvement of recommendation seekers in the process of forming the advising group. Developers of recommender systems should consider increasing users\u27 control over relevant characteristics of the members of this group

Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models

Many complex multi-target prediction problems that concern large target spaces are characterised by a need for efficient prediction strategies that avoid the computation of predictions for all targets explicitly. Examples of such problems emerge in several subfields of machine learning, such as collaborative filtering, multi-label classification, dyadic prediction and biological network inference. In this article we analyse efficient and exact algorithms for computing the top-$K$ predictions in the above problem settings, using a general class of models that we refer to as separable linear relational models. We show how to use those inference algorithms, which are modifications of well-known information retrieval methods, in a variety of machine learning settings. Furthermore, we study the possibility of scoring items incompletely, while still retaining an exact top-K retrieval. Experimental results in several application domains reveal that the so-called threshold algorithm is very scalable, performing often many orders of magnitude more efficiently than the naive approach