Improving Topic Diversity in Recommendation Lists: Marginally or Proportionally?

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10367)

Abstract

Diversifying the recommendation lists in recommendation systems could potentially satisfy user’s needs. Most diversification techniques are designed to recommend the top-k relevant and diverse items, which take the coverage of the user preferences into account. The relevance scores are usually estimated by methods such as latent matrix factorization. While in this paper, we model the users’ interests with the topic distributions on the rated items. And then we investigate how to improve the topic diversification within the recommendation lists. We first estimate the topic distributions of users and items through training Latent Dirichlet Allocation (LDA) on the rating set. After that we propose two topic diversification methods based on submodular function maximization and proportionality respectively. Experimental results on MovieLens and FilmTrust datasets demonstrate that our approach outperforms state-of-the-art techniques in terms of distributional diversity.

Keywords

Recommender system Diversity LDA 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Intelligent Information Processing, School of Computer ScienceFudan UniversityShanghaiChina

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