KeywordsRecommender systems, missing data, data quality, preference models
The goal of recommender systems is to suggest items (films, books, etc.) that are specifically relevant to each user. Those personalized recommendations are built upon preferences expressed either explicitely or implicitely, using techniques such as collaborative filtering [1, 2].
However, these techniques rely on data that are often of poor quality, often incomplete, noisy or incoherent data, possibly coming from multiple sources, including social networks. For example, in film recommendations the users may tend to only rate movies that they really liked (or the opposite), which creates data that are missing, but not at random [3, 4]. The goal of this work is to study various methods to exploit such data, to extract reliable information about users preferences.
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