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Erschienen in:
2017 | OriginalPaper | Buchkapitel
Improving Topic Diversity in Recommendation Lists: Marginally or Proportionally?
verfasst von : Xiaolu Xing, Chaofeng Sha, Junyu Niu
Erschienen in: Web and Big Data
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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.