基于异构邻域聚合的协同过滤推荐算法

doi:10.16451/j.cnki.issn1003-6059.202108004

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (757 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract In traditional collaborative filtering models, the feature vector generated by one-hot encoding is sparsely informative. Heterogeneous behavior data is only employed to describe the relationship between different behaviors and the relationship between behaviors of different users is ignored.Aiming at these problems, an algorithm of collaborative filtering with heterogeneous neighborhood aggregation is proposed. Firstly, the heterogeneous interaction between users and items is modeled by the graph, and neighborhoods are built through the connectivity of graph. Then, the neighborhood information integrated by the lightweight graph convolution method is merged into the feature vectors of the target users and items. Finally, the feature vectors of users and items integrating with neighborhood information are input into a multi-task heterogeneous network for training. The problem of data sparseness is alleviated by enriching the hidden information of feature vectors. Experiments on the datasets prove that the performance of the proposed model is better.

Key words： Heterogeneous Data Neighborhood Aggregation Collaborative Filtering Recommender System Graph Neural Network

Received: 13 May 2021

ZTFLH:

TP 391

Fund:National Natural Science Foundation of China(No.61972182)

Corresponding Authors: XIA Hongbin, Ph.D., associate professor. His research interests include personalized recommendation, natural language processing and network optimization.

About author:: LU Wei, master student. His research interests include deep learning and recommendation system.LIU Yuan, master, professor. His research interests include network security and social network.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	XIA Hongbin
	LU Wei
	LIU Yuan

Cite this article:

XIA Hongbin,LU Wei,LIU Yuan. Collaborative Filtering with Heterogeneous Neighborhood Aggregation[J]. , 2021, 34(8): 712-722.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202108004 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I8/712

[1] 罗洋,夏鸿斌,刘渊.融合注意力LSTM的协同过滤推荐算法.中文信息学报, 2019, 33(12): 110-118.
(LUO Y, XIA H B, LIU Y. Collaborative Filtering Based on Attention LSTM. Journey of Chinese Information Processing, 2019, 33(12): 110-118.)
[2] 黄立威,江碧涛,吕守业,等.基于深度学习的推荐系统研究综述.计算机学报, 2018, 41(7): 1619-1647.
(HUANG L W, JIANG B T, LÜ S Y, et al. Survey on Deep Lear-ning Based Recommender Systems. Chinese Journal of Computers, 2018, 41(7): 1619-1647.)
[3] ADOMAVICIUS G, TUZHILIN A. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6): 734-749.
[4] RENDLE S, FREUDENTHALER C, GANTNER Z, et al. BPR: Bayesian Personalized Ranking from Implicit Feedback // Proc of the 25th Conference on Uncertainty in Artificial Intelligence. New York, USA: ACM, 2009: 452-461.
[5] HE X N, LIAO L Z, ZHANG H W, et al. Neural Collaborative Filtering // Proc of the 26th International Conference on World Wide Web. New York, USA: ACM, 2017: 173-182.
[6] EBESU T, SHEN B, FANG Y. Collaborative Memory Network for Recommendation Systems // Proc of the 41st ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2018: 515-524.
[7] KABBUR S, NING X, KARYPIS G. FISM: Factored Item Similarity Models for Top-N Recommender Systems // Proc of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2013: 659-667.
[8] CHEN J Y, ZHANG H W, HE X N, et al. Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention // Proc of the 40th ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2017: 335-344.
[9] MA C, MA L H, ZHANG Y X, et al. Memory Augmented Graph Neural Networks for Sequential Recommendation // Proc of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2019, 34(4): 5045-5052.
[10] QU Y R, BAI T, ZHANG W N, et al. An End-to-End Neighborhood-Based Interaction Model for Knowledge-Enhanced Recommendation // Proc of the 1st International Workshop on Deep Learning Practice for High-Dimensional Sparse Data. New York, USA: ACM, 2019. DOI: 10.1145/3326937.3341257.
[11] WANG X, HE X N, WANG M, et al. Neural Graph Collaborative Filtering // Proc of the 42nd ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2019: 165-174.
[12] HE X N, DENG K, WANG X, et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation // Proc of the 43rd ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2020: 639-648.
[13] CHONG C, ZHANG M, LIU Y, et al. Neural Attentional Rating Regression with Review-Level Explanations // Proc of the 28th International Conference on World Wide Web. New York, USA: ACM, 2018: 1583-1592.
[14] 陈碧毅,黄玲,王昌栋,等.融合显式反馈与隐式反馈的协同过滤推荐算法.软件学报, 2020, 31(3): 794-805.
(CHEN B Y, HUANG L, WANG C D, et al. Explicit and Implicit Feedback Based Collaborative Filtering Algorithm. Journal of Software, 2020, 31(3): 794-805.)
[15] ZHAO Z, CHENG Z Y, HONG L C, et al. Improving User Topic Interest Profiles by Behavior Factorization // Proc of the 24th International Conference on World Wide Web. New York, USA: ACM, 2015: 1406-1416.
[16] GAO C, HE X N, GAN D H, et al. Neural Multi-task Recommendation from Multi-behavior Data // Proc of the 35th IEEE International Conference on Data Engineering. Washington, USA: IEEE, 2019: 1554-1557.
[17] CHEN C, ZHANG M, ZHANG Y F, et al. Efficient Heteroge-neous Collaborative Filtering without Negative Sampling for Reco-mmendation. Proceeding of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 19-26.
[18] YUAN F J, XIN X, HE X N, et al. f_BGD: Learning Embeddings from Positive Unlabeled Data with BGD[C/OL]. [2021-05-01]. https://fajieyuan.github.io/papers/fBGD2018.pdf.
[19] WANG X, WANG R J, SHI C, et al. Multi-component Graph Convolutional Collaborative Filtering[C/OL]. [2021-05-01]. https://arxiv.org/pdf/1911.10699.pdf.
[20] KIP F T N, WELLING M. Semi-supervised Classification with Graph Convolutional Networks[C/OL]. [2021-05-01]. https://arxiv.org/pdf/1609.02907v4.pdf.
[21] LIU N, SHEN B. Aspect-Based Sentiment Analysis with Gated Alternate Neural Network. Knowledge-Based Systems, 2019, 188. DOI: 10.1016/j.knosys.2019.105010.
[22] WANG X, JIN H Y, ZHANG A, et al. Disentangled Graph Co-llaborative Filtering // Proc of the 43rd ACM SIGIR International Conference on Research and Development in Information Retrieval. New York, USA: ACM, 2020: 1001-1010.
[23] CHEN H, ZHANG M, LIU Y Q, et al. Social Attentional Memory Network: Modeling Aspect- and Friend-Level Differences in Re-commendation // Proc of the 12th ACM International Conference on Web Search and Data Mining. New York, USA: ACM, 2019: 177-185.
[24] LIANG D W, CHARLIN L, MCINERNEY J, et al. Modeling User Exposure in Recommendation // Proc of the 25th International Conference on World Wide Web. New York, USA: ACM, 2016: 951-961.
[25] HARPER F M, KONSTAN J A. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 2016, 5(4): 19:1-19:19.
[26] ZHU H, LI X, ZHANG P Y, et al. Learning Tree-Based Deep Model for Recommender Systems // Proc of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2018: 1079-1088.
[27] LONI B, PAGANO R, LARSON M, et al. Bayesian Personalized Ranking with Multi-channel User Feedback // Proc of the 10th ACM Conference on Recommender Systems. New York, USA: ACM, 2016: 361-364