融合领导者影响与隐式信任度的群组推荐方法

doi:10.3778/j.issn.1002-8331.2107-0011

摘要/Abstract

摘要： 群组推荐需要同时考虑一组内所有成员的偏好，融合偏好进而向群组推荐项目。现有关于群组推荐方法的研究中大多使用固定的、对称的关系权重进行预测评分，忽略了群体成员之间复杂的关系影响，这会导致组推荐准确度偏低。为了解决上述问题，提出了一种融合领导者影响与隐式信任度的群组推荐方法（GRS-IT），该方法通过模糊C均值聚类与皮尔逊相关性结合的方法，发现高相似度群组，有效地提高群组推荐效果和稳定性；引入领导者影响的方法，结合皮尔逊相关性与一种隐式信任度计算找出组内领导者并获取领导者与成员、成员彼此之间的动态影响权重，降低群组推荐的误差率；此外，该方法将基于人类遗忘曲线的时间函数融入到项目评分预测中，预测评分随时间变化赋予不同的时间权重值，进一步提高了群组推荐的准确性。采用对比实验对GRS-IT的有效性进行验证，结果表明，在MovieLens100K数据集上与其他群组推荐方法相比，推荐结果在准确度和群组成员满意度方面都有显著提高。

Abstract: Group recommendation needs to consider the preferences of all members of a group at the same time, and then integrate the preferences to recommend items to the group. Most of the existing researches on group recommendation methods use fixed and symmetric relationship weights to predict scores, ignoring the complex relationship influence among group members, which will lead to low accuracy of group recommendation. In order to solve the above problems, a group recommendation method（GRS-IT） is proposed, which integrates leader influence and implicit trust. By combining fuzzy C-means clustering with Pearson correlation, the method can find groups with high similarity, which can effectively improve the effect and stability of group recommendation. By introducing the method of leader influence, combining Pearson correlation and an implicit trust calculation, the leaders in the group are found out and the dynamic influence weights between leaders and members and between members are obtained to reduce the error rate of group recommendation. In addition, this method integrates the time function based on the human forgetting curve into the prediction of item rating, and gives different time weight values over time to the prediction score, which further improves the accuracy of group recommendation. Finally, a comparative experiment is used to verify the effectiveness of GRS-IT. The results show that, compared with other group recommendation methods on the Movielens100K data set, the recommendation results are significantly improved in terms of accuracy and group member satisfaction.

Key words: group recommendation, fuzzy C-means algorithm, leader influence, implicit trust, time function

王永贵, 林佳敏, 何佳玉. 融合领导者影响与隐式信任度的群组推荐方法[J]. 计算机工程与应用, 2022, 58(9): 98-106.

WANG Yonggui, LIN Jiamin, HE Jiayu. Group Recommendation Method Combining Leader Influence and Implicit Trust Metrics[J]. Computer Engineering and Applications, 2022, 58(9): 98-106.

参考文献

[1] NTOUTSI E，STEFANIDIS K，KJETIL G，et al.Fast group recommendations by applying user clustering[C]//International Conference on Conceptual Modeling，2012：126-140.
[2] HAMMOU B A，LAHCEN A A，MOULINE S.A distributed group recommendation system based on extreme gradient boosting and big data technologies[J].Applied Intelligence，2019，49（12）：4128-4149.
[3] CONTRERAS D，SALAMO M，PASCUAL J.A web-based environment to support online and collaborative group recommendation scenarios[J].Applied Artifical Intelligence，2015，29（5）：480-499.
[4] 陶永才，曹朝阳，石磊，等.一种结合时间因子聚类的群组兴趣点推荐模型[J].小型微型计算机系统，2020，41（2）：356-360.
TAO Y C，CAO Z Y，SHI L，et al.Group POI recommendation model based on time factor clustering[J].Journal of Chinese Computer Systems，2020，41（2）：356-360.
[5] CHOUDHARY N，MINZ S，BHARADWAJ K K.Circle-based group recommendation in social networks[J].Soft Computing，2020（3）.
[6] MCCARTHY K，SALAMó M，COYLE L，et al.CATS：a synchronous approach to collaborative group recommendation[C]//Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference，Melbourne Beach，Florida，USA，May　2006：11-13.
[7] MáRQUEZ J O á，ZIEGLER J.Hootle+：a group recommender system supporting preference negotiation[C]//CYTED-RITOS International Workshop on Groupware，2016.
[8] CHRISTENSEN I，SCHIAFFINO S，ARMENTANO M.Social group recommendation in the tourism domain[J].Journal of Intelligent Information Systems，2016，47（2）：209-231.
[9] ZHAO P P，ZHU H F，LIU Y，et al.A generative model approach for geo-social group recommendation[J].Journal of Computer Science and Technology，2018，33（4）：727-738.
[10] 席茜，张凤琴，李小青.结合用户组群和隐性信任的概率矩阵分解推荐[J].计算机工程与应用，2019，55（2）：137-141.
XI X，ZHANG F Q，LI X Q.Probabilistic matrix factorization recommendation with user group and implicit trust[J].Computer Engineering and Applications，2019，55（2）：137-141.
[11] GUO Y，ZI Y，JIANG Y.Contrastive study of distributed multitask fuzzy c-means clustering and traditional clustering algorithms[C]//2020 5th International Conference on Communication，Image and Signal Processing（CCISP），2020.
[12] 王海艳，肖亦康.基于密度峰值聚类的动态群组发现方法[J].计算机研究与发展，2018，55（2）：391-399.
WANG H Y，XIAO Y K.Dynamic group discovery based on density peaks clustering[J].Journal of Computer Research and Development，2018，55（2）：391-399.
[13] KHANG T D，VUONG N D，TRAN M K，et al.Fuzzy c-means clustering algorithm with multiple fuzzification coefficients[J].Algorithms，2020，13（7）：158.
[14] SARWAR B，KARYPIS G，KONSTAN J，et al.Item　based collaborative filtering[J].Internet Computing，IEEE，2003，7（1）：76-80.
[15] RUAN Y，DURRESI A.A survey of trust management systems for online social communities-trust modeling，trust inference and attacks[J].Knowledge-Based Systems，2016，106：150-163.
[16] GOHARI F S，ALIEE F S，HAGHIGHI H.A new confidence-based recommendation approach：combining trust and certainty[J].Information Sciences，2017，422：21-50.
[17] HU S G，LIU Y，CHEN T P，et al.Emulating the Eb-binghaus forgetting curve of the human brain with a NiO-based memristor[J].Applied Physics Letters，2013，103（13）：734.
[18] 王永贵，刘凯奇.一种优化聚类的协同过滤推荐算法[J].计算机工程与应用，2020，56（15）：66-73.
WANG Y G，LIU K Q.A collaborative filtering recommendation algorithm for clustering optimization[J].Computer Engineering and Applications，2020，56（15）：66-73.
[19] 吴云昌，刘柏嵩，王洋洋.群组推荐分析与研究综述[J].电信科学，2018，34（12）：71-83.
WU Y C，LIU B S，WANG Y Y.Review of group recomm-endation analysis and research[J].Telecommunications Science，2018，34（12）：71-83.
[20] MOVAGHAR A.Centrality-based group formation in group recommender systems[C]//International Conference on World Wide Web Companion，International World Wide Web Conferences Steering Committee，2017.
[21] VILLAVICENCIO C，SCHIAFFINO S，DIAZ-PACE J A，et al.A MAS approach for group recommendation based on negotiation techniques[C]//Advances in Practical Applications of Scalable Multi-agent Systems，The PAAMS Collection，2016.
[22] BORATTO L，CARTA S，FENU G.Discovery and representation of the preferences of automatically detected groups：exploiting the link between group modeling and clustering[J].Future Generation Computer Systems，2016，64（2）：165-174.
[23] AGARWAL A，CHAKRABORTY M，CHOWDARY C R.Does order matter? Effect of order in group recommendation[J].Expert Systems with Applications，2017，82（10）：115-127.
[24] SENOT C，KOSTADINOV D，BOUZID M，et al.Analysis of strategies for building group profiles[C]//International Conference on User Modeling Adaptation，and Personalization，2010.
[25] PESSEMIER T D，DOOMS S，MARTENS L.Comparison of group recommendation algorithms[J].Multimedia Tools and Applications，2014，72（3）：2497-2541.
[26] BARZEGAR R，KOOHI H，MAHMODI E.A novel trust computation method based on user ratings to improve the recommendation[J].International Journal of Engineering，2020，33（3）:377-386.