基于标签权重的协同过滤推荐算法
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摘要
针对传统协同过滤推荐算法中由于相似度计算导致推荐精度不足的问题,提出一种基于标签权重相似度量方法的协同过滤推荐算法。首先,通过改进当前算法中标签权重的计算,并构成用户-标签权重矩阵和物品-标签权重矩阵;其次,考虑到推荐系统是以用户为中心进行推荐,继而通过构建用户-物品关联矩阵来获取用户对物品最准确的评价和需求;最后,根据用户-物品的二部图,利用物质扩散算法计算基于标签权重的用户间相似度,并为目标用户生成推荐列表。实验结果表明,与一种基于"用户-项目-用户兴趣标签图"的协同好友推荐算法(UITGCF)相比,在稀疏度环境为0.1时该算法的召回率、准确率和F1值分别提高了14.69%、9.44%、17.23%。当推荐项目数量为10时,三个指标分别提高了17.99%、8.98%、16.27%。结果表明基于标签权重的协同过滤推荐算法可有效提高推荐结果。
Aiming at the problem that the recommendation accuracy is not good enough due to the similarity calculation in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on the similarity measurement method of tag weight was proposed. Firstly, the calculation of tag weights in existing algorithm was improved to construct a user-tag weight matrix and an item-tag weight matrix. Secondly, as the recommendation system is based on the user-centered recommendation, the most accurate evaluation and demand of the users were obtained by constructing a user-item association matrix. Finally, according to the user-item bipartite graph, the similarity between users based on the label weight was calculated by the material diffusion algorithm, and the recommendation lists were generated for the target users. The experimental results show that compared with UITGCF(a hybrid Collaborative Filtering recommendation algorithm by combining the diffusion on User-Item-Tag Graph and users' personal interest model), when the sparsity environment is 0.1, the recall, accuracy, F1 score of the proposed algorithm were respectively increased by 14.69%, 9.44% and 17.23%. When the recommendation item number is 10, the three indicators respectively were increased by 17.99%, 8.98%, and 16.27%. The results show that the collaborative filtering recommendation algorithm based on tag weight effectively improves the recommendation results.
Aiming at the problem that the recommendation accuracy is not good enough due to the similarity calculation in traditional collaborative filtering recommendation algorithm, a collaborative filtering recommendation algorithm based on the similarity measurement method of tag weight was proposed. Firstly, the calculation of tag weights in existing algorithm was improved to construct a user-tag weight matrix and an item-tag weight matrix. Secondly, as the recommendation system is based on the user-centered recommendation, the most accurate evaluation and demand of the users were obtained by constructing a user-item association matrix. Finally, according to the user-item bipartite graph, the similarity between users based on the label weight was calculated by the material diffusion algorithm, and the recommendation lists were generated for the target users. The experimental results show that compared with UITGCF(a hybrid Collaborative Filtering recommendation algorithm by combining the diffusion on User-Item-Tag Graph and users' personal interest model), when the sparsity environment is 0.1, the recall, accuracy, F1 score of the proposed algorithm were respectively increased by 14.69%, 9.44% and 17.23%. When the recommendation item number is 10, the three indicators respectively were increased by 17.99%, 8.98%, and 16.27%. The results show that the collaborative filtering recommendation algorithm based on tag weight effectively improves the recommendation results.
引文
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[4]GUPTA Y,SAINI A,SAXENA A K.A new fuzzy logic based ranking function for efficient information retrieval system[J].Expert Systems with Applications,2015,42(3):1223-1234.
[5]王鑫,黄忠义.网络资源中基于K-Means聚类的个性化推荐[J].北京邮电大学学报,2014,37(S1):120-124.(WANG X,HUANG Z Y.Network resource personalized recommendation based on K-means clustering[J].Journal of Beijing University of Posts and Telecommunications,2014,37(S1):120-124.)
[6]OH K J,LEE W J,LIM C G,et al.Personalized news recommendation using classified keywords to capture user preference[C]//Processing of the 16th International Conference on Advanced Communication Technology.Piscataway,NJ:IEEE,2014:1283-1287.
[7]POLATIDIS N,GEORGIADIS C K.A multi-level collaborative filtering method that improves recommendations[J].Expert Systems with Applications,2018,48:100-110.
[8]LI Y M,WU C T,LAI C Y.A social recommender mechanism for e-commerce:combining similarity,trust,and relationship[J].Decision Support Systems,2013,55(3):740-752.
[9]SHI Y,LARSON M,HANJALIC A.Mining contextual movie similarity with matrix factorization for context-aware recommendation[J].ACM Transactions on Intelligent Systems and Technology,2013,4(1):Article No.16.
[10]SHI Y,LARSON M,HANJALIC A.Mining mood-specific movie similarity with matrix factorization for context-aware recommendation[C]//Proceedings of the 2010 Workshop on Context-aware Movie Recommendation.New York:ACM,2010:34-40.
[11]HU J,GAO Z,PAN W.Multiangle social network recommendation algorithms and similarity network evaluation[EB/OL].[2018-06-02].http://www.emis.ams.org/journals/HOA/JAM/Volume2013/248084.pdf.
[12]RICCI F,ROKACH L,SHAPIRA B.Introduction to Recommender Systems Handbook[M].Berlin:Springer,2011:1-35.
[13]廖天星,王玲.融合项目标签相似性的协同过滤推荐算法[J].计算机应用,2018,38(4):1007-1011.(LIAO T X,WANG L.Collaborative filtering recommendation algorithm combined with item tag similarity[J].Journal of Computer Applications,2018,38(4):1007-1011.)
[14]ZHANG C,WANG K,LIM E,et al.Are features equally representative?A feature-centric recommendation[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence.Menlo Park,CA:AAAI Press,2015:389-395.
[15]陈洁敏,李建国,汤非易,等.融合用户-项目-用户兴趣标签图的协同好友推荐算法[J].计算机科学与探索,2018,12(1):92-100.(CHEN J M,LI J G,TANG F Y,et al.Combining user-item-tag tripartite graph and users'personal interests for friends recommendation[J].Journal of Frontiers of Computer Science and Technology,2018,12(1):92-100.)
[16]陈杰,刘学军,李斌,等.一种基于用户动态兴趣和社交网络的微博推荐方法[J].电子学报,2017,45(4):898-905.(CHEN J,LIU X J,LI B,et al.Personalized microblogging recommendation based on dynamic interests and social networking of users[J].Acta Electronica Sinica,2017,45(4):898-905.)