基于深度学习的内容推荐算法研究综述

doi:10.19678/j.issn.1000-3428.0060557

摘要/Abstract

摘要： 推荐系统是学习用户偏好，实现个性化推荐的系统化应用技术，在商品购买、影音推荐、关联阅读等多领域得到了广泛的应用。近年来，随着多源异构数据的激增和深度学习的兴起，传统推荐算法中的表征学习模式逐步被深度学习代替。梳理推荐算法的背景和发展趋势，并给出内容推荐的算法思路及其优劣评价，分别介绍多层感知机、自动编码器、卷积神经网络以及循环神经网络等深度学习方法的网络结构和算法优势。从技术应用的视角综述深度学习在内容推荐中的应用现状与研究成果，对不同经典深度推荐算法进行分析与比较。在此基础上，指出深度学习在可解释性、学习效率等方面的不足，并对交叉领域学习、多任务学习、表征学习等未来研究方向进行展望。

关键词: 推荐系统, 表征学习, 内容推荐, 深度学习, 多源异构

Abstract: Recommendation System(RS) is a systematic application technology that learns user preferences to realize personalized recommendation.It has been widely used in commodity purchase,audio and video recommendation,and associated reading.In recent years,the representation learning mode in traditional recommendation algorithms has been gradually replaced by deep learning along with the proliferation of multi-source heterogeneous data and the rise of deep learning.This paper describes the background and development of the recommendation algorithms,discussing the basic ideas of the algorithms,as well as their advantages and disadvantages.The paper particularly focuses on the deep learning-based methods,introducing their merits and network structures,including the multilayer perceptron,auto-encoder,convolutional neural network and recurrent neural network.Then the paper summarizes the application of deep learning in content recommendation and the findings of relevant studies,analyzing and comparing the classical deep recommendation algorithms.On this basis,the shortcomings of deep learning in terms of interpretability and learning efficiency are pointed out,and the future research directions in cross-domain learning,multi-task learning and representation learning are discussed.

Key words: Recommendation System(RS), representation learning, content recommendation, deep learning, multi-source heterogeneity

中图分类号:

TP181

刘华玲, 马俊, 张国祥. 基于深度学习的内容推荐算法研究综述[J]. 计算机工程, 2021, 47(7): 1-12.

LIU Hualing, MA Jun, ZHANG Guoxiang. Review of Studies on Deep Learning-Based Content Recommendation Algorithms[J]. Computer Engineering, 2021, 47(7): 1-12.

https://www.ecice06.com/CN/Y2021/V47/I7/1

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