Review Regularized Neural Collaborative Filtering

• URL: http://arxiv.org/abs/2008.13527v1
• Date: Thu, 20 Aug 2020 18:54:27 GMT
• Title: Review Regularized Neural Collaborative Filtering
• Authors: Zhimeng Pan, Wenzheng Tao, Qingyao Ai
• Abstract summary: We propose a flexible neural recommendation framework, named Review Regularized Recommendation, short as R3. It consists of a neural collaborative filtering part that focuses on prediction output, and a text processing part that serves as a regularizer. Our preliminary results show that by using a simple text processing approach, it could achieve better prediction performance than state-of-the-art text-aware methods.
• Score: 11.960536488652354
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, text-aware collaborative filtering methods have been proposed to address essential challenges in recommendations such as data sparsity, cold start problem, and long-tail distribution. However, many of these text-oriented methods rely heavily on the availability of text information for every user and item, which obviously does not hold in real-world scenarios. Furthermore, specially designed network structures for text processing are highly inefficient for on-line serving and are hard to integrate into current systems. In this paper, we propose a flexible neural recommendation framework, named Review Regularized Recommendation, short as R3. It consists of a neural collaborative filtering part that focuses on prediction output, and a text processing part that serves as a regularizer. This modular design incorporates text information as richer data sources in the training phase while being highly friendly for on-line serving as it needs no on-the-fly text processing in serving time. Our preliminary results show that by using a simple text processing approach, it could achieve better prediction performance than state-of-the-art text-aware methods.

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