Shlomo Berkovsky

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The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event on Context-Awareness in Recommender Systems at the 2010 ACM Recommender Systems conference. The challenge focused on three context-aware recommendation tasks: time-based, mood-based, and social recommendation. The participants were provided with anonymized(More)
Collaborative filtering recommendations were designed primarily for individual user models and recommendations. However, nowadays more and more scenarios evolve, in which the recommended items are consumed by groups of users rather than by individuals. This raises the need to uncover the most appropriate group-based collaborative filtering recommendation(More)
Provision of personalized recommendations to users requires accurate modeling of their interests and needs. This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems. Such a process is defined as user models(More)
Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to(More)
As the obesity epidemic takes hold across the world many medical professionals are referring users to online systems aimed at educating and persuading users to alter their lifestyle. The challenge for many of these systems is to increase initial adoption and sustain participation for sufficient time to have real impact on the life of its users. In this work(More)
One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users' ratings stored by collaborative systems(More)
Contemporary lifestyle has become increasingly sedentary: little physical (sports, exercises) and much sedentary (TV, computers) activity. The nature of sedentary activity is self-reinforcing, such that increasing physical and decreasing sedentary activity is difficult. We present a novel approach aimed at combating this problem in the context of computer(More)
Personalized technologies aim to enhance user experience by taking into account users’ interests, preferences, and other relevant information. Persuasive technologies aim to modify user attitudes, intentions, or behavior through computer-human dialogue and social influence. While both personalized and persuasive technologies influence user interaction(More)
User data scarcity has always been indicated among the major problems of collaborative filtering recommender systems. That is, if two users do not share sufficiently large set of items for whom their ratings are known, then the user-to-user similarity computation is not reliable and a rating prediction for one user can not be based on the ratings of the(More)
Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users, according to their characteristics and preferences, as represented by a User Model (UM). Since the(More)