Paper 88 (Research track)

Leveraging the Dynamic Changes from Items to Improve Recommendation

Author(s): Zongze Jin, Yun Zhang, Weimin Mu, Weiping Wang, Hai Jin

Abstract: User-generated reviews contain rich information, which has been ignored by most of recommender systems. Recently, some recommender systems using reviews with deep learning techniques have demonstrated that they can potentially alleviate the sparsity problem and improve the quality of recommendation. However, they only consider the dynamic interests from users but ignoring the changed properties of items. In this paper, we present a deep model which can capture not only the common users’ behaviors, the changed users’ interests and fundamental item properties, but also the changed properties of items. Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms all baseline recommender systems.

Keywords: Recommender System; Dynamic Item Reviews; Deep Learning

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