Review Regularized Neural Collaborative Filtering

TL;DR

This paper introduces R3, a flexible neural recommendation framework that leverages review texts as regularizers during training, improving prediction accuracy without requiring text processing during online serving.

Contribution

The proposed R3 framework effectively integrates review texts as regularizers, enhancing prediction performance while maintaining efficiency for real-time recommendation systems.

Findings

R3 outperforms state-of-the-art text-aware methods in prediction accuracy.

The modular design simplifies online serving by avoiding real-time text processing.

Using simple text processing approaches yields significant performance gains.

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.