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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Modeling Item Categories for Effective Recommendation
verfasst von : Bo Song, Yi Cao, Weike Pan, Congfu Xu
Erschienen in: Database Systems for Advanced Applications
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Abstract
One-class collaborative filtering and item cold-start are two of the most important and challenging problems in recommender systems. In this paper, we focus on addressing these two issues by taking item category information into account. Item categories embody rich information about product attributes, which are available in most E-commerce websites. However, existing methods usually ignore such information or utilize them at a shallow level. For example, the category information is used to regularize model parameters or to extract hand-crafted features. As a response, we propose to model users’ different preference spaces over different item domains. Specifically, we design a unified method called CatRec in order to model the complex interactions among a user, an item and the item’s category information. Empirically, our method consistently outperforms the state-of-the-art methods on two real-world datasets.