Advances and Challenges of Multi-task Learning Method in Recommender System: A Survey

TL;DR

This survey reviews recent advances in multi-task learning methods for recommender systems, categorizing techniques and discussing future research directions to aid understanding and development in this field.

Contribution

It provides the first comprehensive taxonomy and systematic overview of multi-task learning approaches specifically applied to recommender systems.

Findings

Summarizes current multi-task learning techniques in recommender systems.

Identifies key challenges and future research directions.

Provides a structured taxonomy of methods.

Abstract

Multi-task learning has been widely applied in computational vision, natural language processing and other fields, which has achieved well performance. In recent years, a lot of work about multi-task learning recommender system has been yielded, but there is no previous literature to summarize these works. To bridge this gap, we provide a systematic literature survey about multi-task recommender systems, aiming to help researchers and practitioners quickly understand the current progress in this direction. In this survey, we first introduce the background and the motivation of the multi-task learning-based recommender systems. Then we provide a taxonomy of multi-task learning-based recommendation methods according to the different stages of multi-task learning techniques, which including task relationship discovery, model architecture and optimization strategy. Finally, we raise discussions on the application and promising future directions in this area.