Research Commentary on Recommendations with Side Information: A Survey and Research Directions

TL;DR

This survey reviews recent advances in recommender systems that incorporate side information, highlighting methodologies, data representations, challenges, and future research directions to improve recommendation quality.

Contribution

It provides a comprehensive systematic overview of recent algorithms leveraging side information, covering various methodologies and data types, and discusses future research challenges.

Findings

Side information improves recommendation accuracy.

Deep learning models effectively utilize complex data.

Future directions include handling data sparsity and integrating diverse data types.

Abstract

Recommender systems have become an essential tool to help resolve the information overload problem in recent decades. Traditional recommender systems, however, suffer from data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree of effectiveness in improving recommendation performance. This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information. Specifically, we provide an overview of state-of-the-art recommendation algorithms with side information from two orthogonal perspectives. One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models. The others cover different representations of side information, including structural data (flat, network, and hierarchical features, and knowledge graphs); and non-structural data (text, image and video features). Finally, we discuss challenges and provide new potential directions in recommendation, along with the conclusion of this survey.