Journal of Frontiers of Computer Science and Technology ›› 2024, Vol. 18 ›› Issue (5): 1109-1134.DOI: 10.3778/j.issn.1673-9418.2309016

• Frontiers·Surveys • Previous Articles     Next Articles

Survey on Popularity Based Recommendation

LEI Qinlan, TIAN Xuan   

  1. 1. School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
    2. Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration, Beijing 100083, China
  • Online:2024-05-01 Published:2024-04-29

基于流行的推荐研究综述

雷钦岚,田萱   

  1. 1. 北京林业大学 信息学院,北京 100083
    2. 国家林业草原林业智能信息处理工程技术研究中心,北京 100083

Abstract: Currently, popularity based recommendation has become a research hotspot. The use of popularity considerably improves the recommendation effects, while the Matthew effect caused by popularity bias has also garnered extensive attention among researchers. Some researchers consider combining both aspects to produce hybrid popularity based recommendation. Adopting the concept of popularity, a unified representation of popularity, popularity bias, and hybrid popularity is provided in this paper. Firstly, the background of popularity in the field of recommendation is introduced. Then, based on different perspectives, a comprehensive survey on popularity-enhanced recommendation methods, popularity debias recommendation methods, and hybrid popularity based recommendation methods is provided. Each type of method is further subdivided in specific subtasks of modeling or concrete strategies. The representative models of each method are introduced and analyzed, and their advantages and limitations are evaluated. The mechanisms and applicable scenarios of each method are also summarized in detail. Furthermore, the commonly used datasets, performance evaluation indicators and baseline are introduced. A comparative analysis of the representative methods performance is also listed. Finally, some opinions on the trends of popularity based recommendation are presented. An outlook on the technical difficulties and hotspots for future development from multiple perspectives is analyzed and predicted.

Key words: popularity, popularity bias, hybrid popularity, popularity based recommendation

摘要: 目前,基于流行的推荐系统成为研究热点。流行度使得推荐效果得到显著提升,而流行偏差带来的马太效应也引发了研究者的广泛关注,同时一些研究者考虑将二者结合作为混合式流行来实现推荐。采用流行这一概念,对流行度、流行偏差和混合式流行进行统一表示。首先介绍流行在推荐领域的应用背景,然后根据不同视角,分别对流行度增强推荐方法、去流行偏差推荐方法和混合式流行推荐方法进行综述。在每类方法中,根据建模的具体子任务或具体策略进行进一步划分,对代表性方法进行分析介绍,评价其优点和局限性等,并详细总结每类方法的方法机制和适用场景,从多方面对不同方法间的联系与区别进行讨论。还介绍了该领域中常用数据集、评价指标和基线算法,并对其中代表性方法进行性能对比分析。最后针对基于流行的推荐研究发展趋势提出一些看法,从多角度对该技术未来的发展难点与热点进行总结与展望。

关键词: 流行度, 流行偏差, 混合式流行, 基于流行的推荐