Review Regularized Neural Collaborative Filtering
This addresses the cold start and data sparsity issues in recommendation systems for users and items with limited text data, though it is incremental as it builds on existing neural collaborative filtering approaches.
The paper tackles the problem of text-aware collaborative filtering in recommendations, which often fails in real-world scenarios due to missing text data and inefficient online serving, by proposing a modular neural framework that uses text as a regularizer during training, achieving better prediction performance than state-of-the-art methods.
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.