摘要
针对个性化商品推荐方法中普遍存在的推荐准确率不高的问题,提出一种集成用户信任度和品牌认可度的商品推荐方法(TBCRMI)。该方法通过分析用户的购买行为和评价行为,计算得到用户对商品品牌的认可度和用户自身的活跃度;然后利用DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法对用户进行聚类,并在此基础上融合用户信任关系,采用Top-K方法得到近邻关系;最后,依据近邻关系生成目标用户商品推荐列表。为了验证算法的有效性,使用Amazon Food和Unlocked Mobile phone两个数据集,选择基于用户的协同过滤算法(User CF)、融合用户信任的协同过滤推荐算法(SPTUser CF)与合并用户信任的协同过滤算法(MTUserCF),对准确率、召回率和F1值等指标进行了对比分析。实验结果表明,无论是多品牌综合推荐还是单一品牌推荐,TBCRMI在各项指标均优于目前常用的个性化商品推荐方法。
Concerning the low recommendation accuracy of personalized commodity recommendation methods,a Commodity Recommendation Method Integrating User Trust and Brand Recognition(TBCRMI)was proposed.By analyzing the user s purchase behavior and evaluation behavior,the user s recognition of brands and the activities of users themselves were calculated.Then Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm was used to cluster the users,based on which the user trust relationship was fused,and the nearest neighbors were obtained by Top-K method.Finally,the target user commodity recommendation list was generated based on the nearest neighbors.In order to verify the effectiveness of the algorithm,two datasets(Amazon Food and Unlocked Mobile Phone)were used,User based Collaborative Filtering(UserCF)algorithm,Collaborative Filtering recommendation algorithm with User trust(SPTUserCF)and Merging Trust in Collaborative Filtering(MTUserCF)algorithm were chosen,and the accuracy,recall and F1 value were compared and analyzed.The experimental results show that TBCRMI is superior to the commonly used personalized commodity recommendation methods in either multi-brand comprehensive recommendation or single brand recommendation.
作者
冯勇
韩晓龙
付陈平
王嵘冰
徐红艳
FENG Yong;HAN Xiaolong;FU Chenping;WANG Rongbing;XU Hongyan(School of Information,Liaoning University,Shenyang Liaoning 110036,China)
出处
《计算机应用》
CSCD
北大核心
2018年第10期2886-2891,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(71771110)
辽宁省博士科研启动基金资助项目(20160199)~~
关键词
个性化推荐
品牌认可
聚类
用户信任
近邻
personalized recommendation
brand recognition
clustering
user trust
nearest neighbor
