融合多语义信任度与全局信息的混合推荐算法

doi:10.3778/j.issn.1002-8331.2108-0328

摘要/Abstract

摘要： 数据稀疏问题普遍存在于协同过滤系统，仅考虑共同评分项目上局部上下文信息的相似度度量方法已不具备较高可靠性。为解决上述问题，提出一种融合多语义信任度和全局信息的混合推荐算法（multi semantic trust and global knowledge，MSTGK）。引入加权异构信息网络（weighted heterogeneous information network，WHIN），通过加权元路径处理评分数据、社交关系、用户标签和项目属性对用户信任的影响，挖掘不同语义的信任信息以缓解数据稀疏性问题；考虑项目流行度和用户偏好程度两个全局要素对用户相似度的影响，将其作为权重因子改进了JMSD相似测度，旨在提高相似度计算精度；融合用户的多语义信任度和全局相似度进行综合推荐。在DoubanMovie和Yelp两个真实数据集上的实验结果表明，所提算法缓解了数据稀疏问题，相比于其他基线方法，预测准确率分别提高了2.01个百分点和2.45个百分点。

关键词: 协同过滤, 加权异构信息网络（WHIN）, 加权元路径, 信任关系, 项目流行度

Abstract: The problem of data sparsity generally exists in collaborative filtering system. The similarity measurement which only considers the local context information on the common rating items does not have high reliability. In order to solve the above problems, a hybrid recommendation algorithm MSTGK is proposed, which integrates multi-semantic trust and global knowledge. Firstly, the weighted heterogeneous information network（WHIN） is introduced to deal with the impact of rating data, social relations, user tags and item attributes on user trust through weighted meta path, and mine trust information with different semantics to alleviate the problem of data sparsity. Secondly, considering the influence of popularity of items and user preference on user similarity, they are used as weight factors to improve the JMSD similarity measure in order to improve the accuracy of similarity calculation. Finally, the user’s multi-semantic trust and global similarity are integrated for comprehensive recommendation. Experimental results on two real datasets, DoubanMovie and Yelp, show that the proposed method alleviates data sparsity and compared with other baseline methods, the prediction accuracy is improved by 2.01 and 2.45?percentage points respectively.

Key words: collaborative filtering, weighted heterogeneous information network（WHIN）, weighted meta path, trust relationship, project popularity

王永贵, 蔡永旺, 王阳. 融合多语义信任度与全局信息的混合推荐算法[J]. 计算机工程与应用, 2022, 58(13): 102-111.

WANG Yonggui, CAI Yongwang, WANG Yang. Hybrid Recommendation Algorithm Combining Multi-Semantic Trust and Global Knowledge[J]. Computer Engineering and Applications, 2022, 58(13): 102-111.

参考文献

[1] LIU X C，SU X，MA J M，et al.Information filtering based on eliminating redundant diffusion and compensating balance[J].International Journal of Modern Physics B，2019，33（13）：1950129.
[2] ZHANG H F，ZHOU P，JIAN Y M.Collaborative filtering recommendation algorithm based on class correlation distance[J].Recent Advances in Computer Science and Communications，2021，14（3）：887-894.
[3] MASSA P，AVESANI P.Trust-aware recommender systems[C]//Proceedings of the 2007 ACM Conference on Recommender Systems，2007：17-24.
[4] MORADI P，AHMADIAN S.A reliability-based recommendation method to improve trust-aware recommender systems[J].Expert Systems with Applications，2015，42（21）：7386-7398.
[5] YANG B，LEI Y，LIU J M，et al.Social collaborative filtering by trust[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2017，39（8）：1633-1647.
[6] 徐毅，叶卫根，戴鑫，等.融合用户信任度与相似度的推荐算法研究[J].小型微型计算机系统，2018，39（1）：78-83.
XU Y，YE W G，DAI X，et al.Recommendation algorithm incorporating user trust and user similarity[J].Journal of Chinese Computer Systems，2018，39（1）：78-83.
[7] 李熠晨，陈莉，石晨晨，等.采用信任网络增强的协同过滤算法[J].计算机应用研究，2018，35（1）：116-120.
LI Y C，CHEN L，SHI C C，et al.Enhanced collaborative filtering algorithm adopting trust network[J].Application Research of Computers，2018，35（1）：116-120.
[8] 吴航，江红.融合潜在社交信任模型的协同过滤推荐[J].计算机工程与应用，2019，55（20）：114-121.
WU H，JIANG H.Collaborative filtering recommendation integrating potential social trust model[J].Computer Engineering and Applications，2019，55（20）：114-121.
[9] MA G，WANG Y，ZHENG X，et al.A trust-aware latent space mapping approach for cross-domain recommendation[J].Neurocomputing，2020，431（4）：100-110.
[10] 张岐山，朱猛.融合时间加权信任与用户偏好的协同过滤算法[J].计算机工程与应用，2022，58（3）：112-188.
ZHANG Q S，ZHU M.Collaborative filtering algorithm combining time-weighted trust and user preferences[J].Computer Engineering and Applications，2022，58（3）：112-188.
[11] BOBADILLA J，SERRADILLA F，BERNAL J.A new collaborative filtering metric that improves the behavior of recommender systems[J].Knowledge-Based Systems，2010，23（6）：520-528.
[12] 陈功平，王红.改进Pearson相关系数的个性化推荐算法[J].山东农业大学学报（自然科学版），2016，47（6）：940-944.
CHEN G P，WANG H.A personalized recommendation algorithm on improving Pearson correlation coefficient[J].Journal of Shandong Agricultural University（Natual Science Edition），2016，47（6）：940-944.
[13] BAG S，KUMAR S K，TIWARIM K.An efficient recommendation generation using relevant Jaccard similarity[J].Information Sciences，2019，483：53-64.
[14] 李昆仑，赵佳耀，王萌萌，等.结合半监督AP聚类和改进相似度的推荐算法[J].小型微型计算机系统，2021，42（7）：1396-1401.
LI K L，ZHAO J Y，WAGN M M，et al.Recommendation algorithm combining semi-supervised AP clustering and improved similarity[J].Journal of Chinese Computer Systems，2021，42（7）：1396-1401.
[15] 孙传明，周炎，涂燕.基于混合协同过滤的个性化推荐方法研究[J].华中师范大学学报（自然科学版），2020，54（6）：956-962.
SUN C M，ZHOU Y，TU Y.Research on personalized recommendation method based on hybrid collaborative filtering[J].Journal of Huazhong Normal University（Natural Science），2020，54（6）：956-962.
[16] SUN Y，HAN J，ZHAO P，et al.RankClus：Integrating clustering with ranking for heterogeneous information network analysis[C]//Proceedings of the 12th International Conference on Extending Database Technology Advances in Database Technology，2009：565-576.
[17] 刘云枫，孙平，葛志远.异构信息网络推荐研究进展[J].情报科学，2020，38（6）：151-157.
LIU Y F，SUN P，NIE Z Y.Literature review of heterogeneous information network recommendation[J].Information Science，2020，38（6）：151-157.
[18] SUN Y，HAN J，YAN X，et al.Pathsim：Meta path-based top-k similarity search in heterogeneous information networks[J].Proceedings of the VLDB Endowment，2011，4（11）：992-1003.
[19] SHI C，KONG X N，HUANG Y，et al.HeteSim：A general framework for relevance measure in heterogeneous networks[J].IEEE Transactions on Knowledge and Data Engineering，2014，26（10）：2479-2492.
[20] 姚迪，张超，黄建辉，等.时空数据语义理解：技术与应用[J].软件学报，2018，29（7）：2018-2045.
YAO D，ZHANG C，HUAGN J H，et al.Semantic understanding of spatio-temporal data：Technology & application[J].Journal of Software，2018，29（7）：2018-2045.
[21] SHI C，ZHANG Z Q，JI Y G，et al.SemRec：A personalized semantic recommendation method based on weighted heterogeneous information networks[C]//Proceedings of Conference on World Wide Web，2018：1-32.
[22] LIU H F，HU Z，AHMAD M，et al.A new user similarity model to improve the accuracy of collaborative filtering[J].Knowledge-Based Sysems，2014，56：156-166.
[23] WANG D W，YIH Y，VENTRESCA M.Improving neighbor-based collaborative filtering by using a hybrid similarity measurement[J].Expert Systems with Applications，2020，160：113651.
[24] BREESE J S.Empirical analysis of predictive algorithms for collaborative filtering[C]//Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence，1998：43-52.
[25] LI Y K，LIU J M，REN J D，et al.A novel implicit trust recommendation approach for rating prediction[J].IEEE Access，2020，8：98305-98315.