多样化推荐综述  

Survey on Diversified Recommendation

作　　者：彭迎涛 孟小峰[1] 杜治娟 Peng Yingtao;Meng Xiaofeng;Du Zhijuan(School of Information,Renmin University of China,Beijing 100872;College of Computer Science,Inner Mongolia University,Hohhot 010021)

机构地区：[1]中国人民大学信息学院,北京100872 [2]内蒙古大学计算机学院,呼和浩特010021

出　　处：《计算机研究与发展》2025年第2期285-313,共29页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(62172423,91846204,62162048)。

摘　　要：推荐系统对缓解信息过载问题起着重要的作用,它使得用户从繁杂网络信息(如天猫、TikTok、小红书等)中轻松获取产品和服务.然而多数推荐系统以准确率为中心,导致用户视野受限、部分商家展示机会少、平台内容生态单一且资源信息分配不均衡等不利影响,如引发过滤气泡和马太效应等.由此,提升推荐的多样性逐渐成为推荐系统研究领域的关注重点,其目标是满足人们日益增长的多元化物质生活需求.近年来,推荐系统在多样性方向的技术研究呈现迅速发展态势,然而,目前多样化推荐的研究缺乏系统的整理和归纳,缺少系统地对推荐的多样化问题进行梳理和综述.首先提出了多样化推荐的问题定义、技术框架、分类及其应用场景.其次从4个方面对模型和算法进行了比较和分析.然后总结了多样化推荐的常用数据集和评测指标.最后探讨了该领域中的问题和挑战,以期激发未来创新,推动多样化推荐的发展.The recommender system has a significant role in alleviating information overload,allowing users to conveniently obtain products and services on various application platforms like Tmall,TikTok,and Xiaohongshu.However,most of the recommendation systems focus on the accuracy rate as the center,which leads to adverse effects such as the limitation of users’vision,fewer display opportunities for some merchants,a single content ecosystem of the platform,and an unbalanced allocation of resources and information,such as triggering the filter bubble and the Matthew effect.As a result,strengthening the diversity of the recommendation system has become a key research point to fulfill the increasingly diversified material demands in people’s lives.In recent years,research on diversified recommendations has developed rapidly.However,this aspect needs to be more systematic in organization and summarization.This paper systematically reviews the issue of diversified recommendations within recommendation systems.Firstly,we put forward the problem definition,technical framework,classification,and application scenarios of diversified recommendations.Secondly,we make comparisons and analyses of models and algorithms from four perspectives.Subsequently,we summarize the commonly used datasets and metrics for diversified recommendations.Finally,we deliberate on the problems and challenges in this field to inspire future innovation and promote development.

关 键 词：推荐系统 多样化 深度学习 平衡性 兴趣偏好 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]