计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 159-164.doi: 10.11896/jsjkx.210300263

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于用户覆盖及评分差异的多样性推荐算法

陈壮, 邹海涛, 郑尚, 于化龙, 高尚   

  1. 江苏科技大学计算机学院 江苏 镇江212003
  • 收稿日期:2021-03-25 修回日期:2021-08-07 出版日期:2022-05-15 发布日期:2022-05-06
  • 通讯作者: 邹海涛 (zouhaitao@126.com)
  • 作者简介:(just_chenzhuang@163.com)

Diversity Recommendation Algorithm Based on User Coverage and Rating Differences

CHEN Zhuang, ZOU Hai-tao, ZHENG Shang, YU Hua-long, GAO Shang   

  1. College of Computer,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China
  • Received:2021-03-25 Revised:2021-08-07 Online:2022-05-15 Published:2022-05-06
  • About author:CHEN Zhuang,born in 1995,postgra-duate.His main research interests include recommender system and so on.
    ZOU Hai-tao,born in 1984,Ph.D,lecturer.His main research interests include data mining and information retrieval.

摘要: 传统的推荐算法着重于提高推荐结果的精确度,对于推荐列表的多样性则有所忽略。但很多研究表明,用户对产品的多样性需求也是影响用户体验的重要因素之一。针对该问题,在用户覆盖定义的基础上,提出了一个基于产品评分差异的用户覆盖模型。在生成用户兴趣域(用户覆盖)的过程中,该模型一方面通过构建评分差异矩阵,将不同用户对同一产品评分上的差异与用户覆盖模型有效地结合起来,从而计算得到更精准的用户兴趣域;另一方面,该模型将用户和推荐列表的兴趣域分别映射到两个m维向量上(分别称为用户向量和产品集向量),并将推荐的目标函数向量化,有效地减少了计算过程中迭代的次数。此外,通过探讨用户向量和产品集向量之间的相似度关系,提出了一种新的推荐列表选取策略。基于该方法构建得到的模型可以在一定程度上兼顾推荐精确度与多样性。由于根据用户向量构建匹配的产品集向量是一个NP-hard问题,因此使用贪心算法来求解该问题,贪心算法的有界性有着严谨的理论依据。在两个真实的数据集上进行实验,结果表明,与多样性推荐方面相关的先进算法相比,所提算法具有一定的优势。

关键词: 多样性, 评分差异, 推荐系统, 相似度, 用户覆盖

Abstract: Traditional recommender systems usually focus on improving recommendation accuracy while neglecting the diversity of recommendation lists.However,several studies have shown that,users’ diversity needs also take an important part of their sa-tisfaction.In this paper,a user-coverage model based on item rating differences is proposed.During generating user’s interest domain(user coverage),on the one hand,the model combines rating differences between users across an item with user-coverage model effectively,thus obtaining a more precise interest domain of the user.On the other hand,objective function is constructed in the form of vector by mapping a user’s and an itemset’s interest domain to two m-dimensional vectors (called user vector and itemset vector respectively),which can reduce the number of iterations in the calculation process.In addition,a new items selection strategy is provided by similarity relationship between those two m-dimensional vectors.The proposed model has superior performance in both accuracy and diversity.User vector for a specific user is a constant,however,finding the most matching itemset vector will be an NP-hard problem.During the implementation of the proposed model,a greedy algorithm is chosen to solve the optimization problem based on critical theoretical boundary.Experimental comparisons with the state-of-the-arts related to diversity recommendation in recent years on two real-world data sets demonstrate that the proposed algorithm can effectively improve the diversity of the recommendation.

Key words: Diversity, Rating differences, Recommender systems, Similarity, User coverage

中图分类号: 

  • TP181
[1]NAHTA R,MEENA Y K,GOPALANI D,et al.Embeddingmetadata using deep collaborative filtering to address the cold start problem for the rating prediction task[J].Multimedia Tools and Applications,2021,80(12):18553-18581.
[2]XU J,YAO Y,TONG H,et al.HoORaYs:High-order optimization of rating distance for recommender systems[C]//Procee-dings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17).2017:525-534.
[3]HE Y,ZOU H,YU H,et al.Adaptive and efficient high-orderrating distance optimization model with slack variable[J].Knowledge-Based Systems,2020,205:106228.
[4]ZHANG Q,LIU L,WEN J H.Recommendation Algorithm withField Trust and Distrust Based on SVD[J].Computer Science,2019,46(10):27-31.
[5]MCNEE S M,RIEDL J,KONSTAN J A.Being accurate is not enough:How accuracy metrics have hurt recommender systems[C]//Extended Abstracts Proceedings of the 2006 Conference on Human Factors in Computing Systems.2006:1097-1101.
[6]CREMONESI P,F GARZOTTO,NEGRO S,et al.Looking for “Good” Recommendations:A Comparative Evaluation of Re-commender Systems[M]//Human-computer Interaction-INTERACT,2011.Berlin:Springer,2011:152-168.
[7]CHENG P,WANG S,MA J,et al.Learning to Recommend Accurate and Diverse Items[C]//Proceedings of the 26th International Conference on World Wide Web.2017:183-192.
[8]SHA C,WU X,NIU J.A Framework for Recommending Relevant and Diverse Items[C]//Proceedings of the 25th International Joint Conference on Artificial Intelligence.2016:3868-3875.
[9]PANTELI A,BOUTSINAS B.Improvement of similarity-diversity trade-off in recommender systems based on a facility location model[J].Neural Computing and Applications,2021(5):1-13.
[10]LE W,LIU Q,CHEN E H,et al.Relevance Meets Coverage:A Unified Framework to Generate Diversified Recommendations[J].ACM Transactions on Intelligent Systems and Technology,2016,7(3):1-30.
[11]SALAKHUTDINOV R.Probabilistic Matrix Factorization[C]//Proceedings of the 20th International Conference on Neural Information Processing Systems.2007:1257-1264.
[12]SHAMEEM A P P,NICOLAS U,YVES G.A Coverage-Based Approach to Recommendation Diversity On Similarity Graph[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:15-22.
[13]HE Y,ZOU H,YU H,et al.Diversity-Aware Recommendation by User Interest Domain Coverage Maximization[C]//2019 IEEE International Conference on Data Mining (ICDM).2019:1084-1089.
[14]AZIN A,BRANISLAV K,SHLOMOB,et al.Optimal Greedy Diversity for Recommendation[C]//IJCAI.2015:1742-1748.
[15]HOCHBA,DORIT S.Approximation Algorithms for NP-Hard Problems[J].ACM SIGACT News,1997,28(2):40-52.
[16]FISHER M L,NEMHAUSER G L,WOLSEY L A.An Analysis of Approximations for Maximizing Submodular Set Functions I[J].Mathematical Programming,1978,14(1):265-294.
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