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Using Mutual Influence to Improve Recommendations
[chapter]
2013
Lecture Notes in Computer Science
In this work we show how items in recommender systems mutually influence each other's utility and how it can be explored to improve recommendations. The way we model mutual influence is cheap and can be computed without requiring any source of content information about either items or users. We propose an algorithm that considers mutual influence to generate recommendations and analyse it over different recommendation datasets. We compare our algorithm with the Top − N selection algorithm and
doi:10.1007/978-3-319-02432-5_6
fatcat:oi7lhxh2vjg35orkpux4hnzwmq