A Neural Collaborative Filtering Model Incorporating Neighborhood Information
Ting Bai (baiting <at> ruc (point) edu (point) cn)
Renmin University, Visiting student at RALI
Wednesday 14 March 2018 at 11:30 AM
Salle 3195, Pavillon André-Aisenstadt
Recently, deep neural networks have been widely applied to recommender systems. A representative work is to utilize deep learning for modeling complex user-item interactions. However, similar to traditional latent factor models by factorizing user-item interactions, they tend to be ineﬀective to capture localized information. Localized information, such as neighborhood between items and users, is important to recommender systems in complementing the user-item interaction data. To incorporate neiborhood information, we propose a novel Neighborhood-based Neural Collaborative Filtering model (NNCF). Extensive experiments on three real-world online purchase datasets demonstrate the eﬀectiveness of our model for the implicit recommendation task.
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