Probabilistic Memory-based Collaborative Filtering

Probabilistic Memory-based Collaborative Filtering

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by Kai Yu , Anton Schwaighofer , Volker Tresp , Xiaowei Xu , Hans-peter Kriegel

Venue:	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Citations:	37 - 2 self

BibTeX

@MISC{Yu_probabilisticmemory-based,
    author = {Kai Yu and Anton Schwaighofer and Volker Tresp and Xiaowei Xu and Hans-peter Kriegel},
    title = { Probabilistic Memory-based Collaborative Filtering},
    year = {}
}

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Abstract

Memory-based collaborative filtering (CF) has been studied extensively in the literature and has proven to be successful in various types of personalized recommender systems. In this paper we develop a probabilistic framework for memory-based CF (PMCF). While this framework has clear links with classical memory-based CF, it allows us to find principled solutions to known problems of CF-based recommender systems. In particular, we show that a probabilistic active learning method can be used to actively query the user, thereby solving the “new user problem”. Furthermore, the probabilistic framework allows us to reduce the computational cost of memory-based CF by working on a carefully selected subset of user profiles, while retaining high accuracy. We report experimental results based on two real world data sets, which demonstrate that our proposed PMCF framework allows an accurate and efficient prediction of user preferences.

Keyphrases

probabilistic memory-based collaborative filtering probabilistic framework memory-based cf user profile real world data set efficient prediction cf-based recommender system principled solution new user problem memory-based collaborative filtering user preference clear link high accuracy computational cost classical memory-based cf experimental result personalized recommender system pmcf framework various type