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Proceedings of the NATO Advanced Study Institute on Learning in graphical models
An algorithmic framework for performing collaborative filtering
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IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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A collaborative filtering approach to mitigate the new user cold start problem
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Information Processing and Management: an International Journal
Knowledge-Based Systems
A Multimedia Recommender System
ACM Transactions on Internet Technology (TOIT)
Facing the cold start problem in recommender systems
Expert Systems with Applications: An International Journal
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Recommender systems for automatically suggested items of interest to users have become increasingly essential in fields where mass personalization is highly valued. The popular core techniques of such systems are collaborative filtering, content-based filtering and combinations of these. In this paper, we discuss hybrid approaches, using collaborative and also content data to address cold-start - that is, giving recommendations to novel users who have no preference on any items, or recommending items that no user of the community has seen yet. While there have been lots of studies on solving the item-side problems, solution for user-side problems has not been seen public. So we develop a hybrid model based on the analysis of two probabilistic aspect models using pure collaborative filtering to combine with users' information. The experiments with MovieLen data indicate substantial and consistent improvements of this model in overcoming the cold-start user-side problem.