A Survey on Incremental Update for Neural Recommender Systems

TL;DR

This survey reviews incremental update methods for neural recommender systems, highlighting challenges, existing approaches, evaluation issues, and open research questions in transitioning from static to real-time updating models.

Contribution

It systematically summarizes current research on incremental updates in neural recommender systems and discusses key challenges and open issues in this emerging area.

Findings

Identifies key challenges in real-time updating of recommender systems.

Reviews existing methods and evaluation issues in incremental neural recommenders.

Outlines open research questions for future development.

Abstract

Recommender Systems (RS) aim to provide personalized suggestions of items for users against consumer over-choice. Although extensive research has been conducted to address different aspects and challenges of RS, there still exists a gap between academic research and industrial applications. Specifically, most of the existing models still work in an offline manner, in which the recommender is trained on a large static training set and evaluated on a very restrictive testing set in a one-time process. RS will stay unchanged until the next batch retrain is performed. We frame such RS as Batch Update Recommender Systems (BURS). In reality, they have to face the challenges where RS are expected to be instantly updated with new data streaming in, and generate updated recommendations for current user activities based on the newly arrived data. We frame such RS as Incremental Update Recommender Systems (IURS). In this article, we offer a systematic survey of incremental update for neural recommender systems. We begin the survey by introducing key concepts and formulating the task of IURS. We then illustrate the challenges in IURS compared with traditional BURS. Afterwards, we detail the introduction of existing literature and evaluation issues. We conclude the survey by outlining some prominent open research issues in this area.