Session: Recommending Non‐Standard Items

Chair: Giovanni Semeraro
Date: Wednesday, September 29, 11:00‐13:00

• Breaking out of the box of recommendations: from items to packages

  by Min Xie, Laks V.S. Lakshmanan, Peter T. Wood

  Classical recommender systems provide users with a list of recommendations where each recommendation consists of a single item, e.g., a book or DVD. However, several applications can benefit from a system capable of recommending packages of items, in the form of sets. Sample applications include travel planning with a limited budget (price or time) and twitter users wanting to select worthwhile tweeters to follow given that they can deal with only a bounded number of tweets. In these contexts, there is a need for a system that can recommend top-k packages for the user to choose from.

  Motivated by these applications, we consider composite recommendations, where each recommendation comprises a set of items. Each item has both a value (rating) and a cost associated with it, and the user specifies a maximum total cost (budget) for any recommended set of items. Our composite recommender system has access to one or more component recommender system, focusing on different domains, as well as to information sources which can provide the cost associated with each item. Because the problem of generating the top recommendation (package) is NP-complete, we devise several approximation algorithms for generating top-k packages as recommendations. We analyze their efficiency as well as approximation quality. Finally, using two real and two synthetic data sets, we subject our algorithms to thorough experimentation and empirical analysis. Our findings attest to the efficiency and quality of our approximation algorithms for top-k packages compared to exact algorithms.

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• Automatically building research reading lists

  by Michael D. Ekstrand, Praveen Kannan, James A. Stemper, John T. Butler, Joseph A. Konstan, John T. Riedl

  All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node’s importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.

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• Learning in efficient tag recommendation

  by Marek Lipczak, Evangelos Milios

  The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing efficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems confirmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system efficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources.

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• Recommender algorithms in activity motivating games

  by Shlomo Berkovsky, Jill Freyne, Mac Coombe, Dipak Bhandari

  Physical activity motivating game design encourages players to perform real physical activity in order to gain virtual game rewards. Previous research into activity motivating games showed that they have the potential to motivate players to perform physical activity, while retaining the enjoyment of playing. However, it was discovered that a uniform motivating approach resulted in different levels of activity performed by players of varying gaming skills. In this work we present and evaluate two adaptive recommendation-based techniques, which aim to balance the amount of physical activity performed by players by adapting the level of motivation to their observed gaming skills. Experimental evaluation showed that the adaptive techniques not only increase the amount of activity performed and retain the enjoyment of playing, but also balance the amount of activity performed by players of varying gaming skills and allow for game difficulty to be set in a player-dependent manner.

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