Joint Neural Collaborative Filtering for Recommender Systems

ACM Transactions on Information Systems - Tập 37 Số 4 - Trang 1-30 - 2019
Wanyu Chen1, Fei Cai2, Honghui Chen2, Maarten de Rijke3
1National University of Defense Technology, China and University of Amsterdam, Amsterdam, The Netherlands
2National University of Defense Technology, Changsha, China
3University of Amsterdam, Amsterdam, The Netherlands

Tóm tắt

We propose a Joint Neural Collaborative Filtering (J-NCF) method for recommender systems. The J-NCF model applies a joint neural network that couples deep feature learning and deep interaction modeling with a rating matrix. Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix. Deep interaction modeling captures non-linear user-item interactions with a deep neural network using the feature representations generated by the deep feature learning process as input. J-NCF enables the deep feature learning and deep interaction modeling processes to optimize each other through joint training, which leads to improved recommendation performance. In addition, we design a new loss function for optimization that takes both implicit and explicit feedback, point-wise and pair-wise loss into account. Experiments on several real-world datasets show significant improvements of J-NCF over state-of-the-art methods, with improvements of up to 8.24% on the MovieLens 100K dataset, 10.81% on the MovieLens 1M dataset, and 10.21% on the Amazon Movies dataset in terms of HR@10. NDCG@10 improvements are 12.42%, 14.24%, and 15.06%, respectively. We also conduct experiments to evaluate the scalability and sensitivity of J-NCF. Our experiments show that the J-NCF model has a competitive recommendation performance with inactive users and different degrees of data sparsity when compared to state-of-the-art baselines.

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ACM Transactions on Information Systems

Tập 37 Số 4

1-30

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