Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

TL;DR

This survey reviews deep learning methods for sequential recommendation, categorizing algorithms, analyzing influential factors, and evaluating their effects to guide future research in the field.

Contribution

It provides a systematic categorization of DL-based sequential recommendation algorithms and analyzes key factors affecting their performance.

Findings

Deep learning methods outperform traditional models in sequential recommendation.

Behavioral sequence type significantly influences model effectiveness.

Evaluation demonstrates the impact of various factors on recommendation accuracy.

Abstract

In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.