| 200 | 2 | 224 |
| 下载次数 | 被引频次 | 阅读次数 |
针对传统的网络拓扑构建方法难以很好描述推荐平台中网络用户的情况，提出了一种基于用户“围观”行为的网络构建方法，对构建的网络进行分析。由于传统的标签传播算法易发生标签入侵和算法结果不稳定，改进标签传播算法的标签更新规则，提出一种适用于挖掘推荐网络社区的标签传播算法。利用短视频推荐平台数据集，对所提算法进行了实验论证。结果表明，所提算法挖掘的社区内部兴趣相似度显著高于整个网络中的兴趣相似度，有较高的兴趣内聚性。
Abstract：The traditional network topology construction method in the recommendation platform was difficult to describe the network users. To solve this problem, a network construction method based on the “click” behavior of users was proposed, and the constructed network was analyzed. Aiming at the shortcomings that the traditional label propagation algorithm was prone to label intrusion and unstable algorithm results in the recommendation network, an algorithm that changed the tag update rules of the tag propagation algorithm was proposed, which was suitable for mining recommended network communities. An experiment was conducted using the domestic short video recommendation platform data set. The results showed that the interest similarity within the community mined by the proposed algorithm was significantly higher than that in the whole network, and had high interest cohesion.
[1] 李悦，谢珺，侯文丽，等.融合用户偏好优化聚类的协同过滤推荐算法[J].郑州大学学报(理学版),2020,52(2):29-35.LI Y,XIE J,HOU W L,et al.Collaborative filtering recommendation algorithm based on optimized clustering with user preference[J].Journal of Zhengzhou university (natural science edition),2020,52(2):29-35.
[2] KERNIGHAN B W,LIN S.An efficient heuristic procedure for partitioning graphs[J].The Bell system technical journal,1970,49(2):291-307.
[3] LIU Z,WANG H,WANG G,et al.Link community detection based on ensemble learning[J].Modern physics letters B,2020,34(27):2050293.
[4] RAGHAVAN U N,ALBERT R,KUMARA S.Near linear time algorithm to detect community structures in large-scale networks[J].Physical review E,statistical,nonlinear,and soft matter physics,2007,76(3):036106.
[5] BARBER M J,CLARK J W.Detecting network communities by propagating labels under constraints[J].Physical review E,statistical,nonlinear,and soft matter physics,2009,80(2):026129.
[6] 张霄宏，史爱静，贾慧娟，等.一种优化的标签传播方法[J].小型微型计算机系统，2021,42(1):137-141.ZHANG X H,SHI A J,JIA H J,et al.Optimized label propagation solution[J].Journal of Chinese computer systems,2021,42(1):137-141.
[7] 辛宇，杨静，谢志强.一种面向语义重叠社区发现的Link-Block算法[J].软件学报，2016,27(2):363-380.XIN Y,YANG J,XIE Z Q.Link-block method for the semantic overlapping community detection[J].Journal of software,2016,27(2):363-380.
[8] 刘少鹏，印鉴，欧阳佳，等.基于MB-HDP模型的微博主题挖掘[J].计算机学报，2015,38(7):1408-1419.LIU S P,YIN J,OUYANG J,et al.Topic mining from microblogs based on MB-HDP model[J].Chinese journal of computers,2015,38(7):1408-1419.
[9] 周小平，梁循，张海燕.基于R-C模型的微博用户社区发现[J].软件学报，2014,25(12):2808-2823.ZHOU X P,LIANG X,ZHANG H Y.User community detection on micro-blog using R-C model[J].Journal of software,2014,25(12):2808-2823.
[10] 刘冰玉，王翠荣，王聪，等.基于动态主题模型融合多维数据的微博社区发现算法[J].软件学报，2017,28(2):246-261.LIU B Y,WANG C R,WANG C,et al.Microblog community discovery algorithm based on dynamic topic model with multidimensional data fusion[J].Journal of software,2017,28(2):246-261.
[11] 张中军，于来行，李润川.基于链路结构和转发行为的微博社交网络重叠社区划分方法[J].郑州大学学报(理学版),2021,53(4):69-76.ZHANG Z J,YU L H,LI R C.Overlapping community division method of microblog social network based on link structure and forwarding behavior[J].Journal of Zhengzhou university (natural science edition),2021,53(4):69-76.
[12] SPERLí G.A deep learning based community detection approach[C]//Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing.New York:ACM Press,2019:1107-1110.
[13] CAI B,WANG Y,ZENG L,et al.Edge classification based on convolutional neural networks for community detection in complex network[J].Physica A:statistical mechanics and its applications,2020,556:124826.
[14] ROZEMBERCZKI B,SARKAR R.Fast sequence-based embedding with diffusion graphs[C]//International Workshop on Complex Networks.Cham:Springer International Publishing,2018:99-107.
[15] POULIN V,THéBERGE F.Ensemble clustering for graphs:comparisons and applications[J].Applied network science,2019,4(1):1-13.
[16] CARLETTI T,FANELLI D,LAMBIOTTE R.Random walks and community detection in hypergraphs[J].Journal of physics:complexity,2021,2(1):015011.
[17] ACHARYA D B,ZHANG H M.Community detection clustering via gumbel softmax[J].SN computer science,2020,1(1):262.
[18] BANDYOPADHYAY S,PETER V.Self-expressive graph neural network for unsupervised community detection[EB/OL].(2020-11-28)[2022-03-25].https://arxiv.org/abs/2011.14078.
[19] CHEN X,LIU D,XIONG Z,et al.Learning and fusing multiple user interest representations for micro-video and movie recommendations[J].IEEE transactions on multimedia,2020,23:484-496.
基本信息:
DOI：10.13705/j.issn.1671-6841.2022202
中图分类号:TP391.3
引用信息:
[1]吴日铭,韩益亮,郭凯阳,等.推荐算法背景下兴趣社区挖掘算法[J],2023,55(06):77-83.DOI:10.13705/j.issn.1671-6841.2022202.
基金信息:
国家社会科学基金项目(20XTQ007,2020-XKJJ-B019);; 武警工程大学科研创新团队基金项目(KYTD201805)