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

09/19/2019
by   Zhu Sun, et al.
0

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.

READ FULL TEXT
research
04/30/2018

Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation refers to the personalized recommendation alg...
research
08/30/2023

A Survey on Multi-Behavior Sequential Recommendation

Recommender systems is set up to address the issue of information overlo...
research
02/09/2023

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

Recommendation systems have become popular and effective tools to help u...
research
02/28/2019

Representation Learning for Recommender Systems with Application to the Scientific Literature

The scientific literature is a large information network linking various...
research
12/23/2022

Recommending on Graphs: A Comprehensive Review from Data Perspective

Recent advances in graph-based learning approaches have demonstrated the...
research
07/01/2020

Non-IID Recommender Systems: A Review and Framework of Recommendation Paradigm Shifting

While recommendation plays an increasingly critical role in our living, ...
research
04/04/2022

Automated Machine Learning for Deep Recommender Systems: A Survey

Deep recommender systems (DRS) are critical for current commercial onlin...

Please sign up or login with your details

Forgot password? Click here to reset