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Mining and representing recommendations in actively evolving recommender systems

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.
Original languageEnglish
Title of host publicationInternational Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA
Publication year2010
ISBN (print)978-1-4244-6522-4
ISBN (Electronic)978-1-4244-6521-7
Publication statusPublished - 2010
Externally publishedYes

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