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Improving Collaborative Filtering with Social Influence over Heterogeneous Information Networks

Authors:

Balaji Palanisamy,

Qi ZhangAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 20, Issue 4

Article No.: 36, Pages 1 - 29

https://doi.org/10.1145/3397505

Published: 15 October 2020 Publication History

Abstract

The advent of social networks and activity networks affords us an opportunity of utilizing explicit social information and activity information to improve the quality of recommendation in the presence of data sparsity. In this article, we present a social-influence-based collaborative filtering (SICF) framework over heterogeneous information networks with three unique features. First, we integrate different types of entities, links, attributes, and activities from rating networks, social networks, and activity networks into a unified social-influence-based collaborative filtering model through the intra-network and inter-network social influence. Second, we propose three social-influence propagation models to capture three kinds of information propagation within heterogeneous information networks: user-based influence propagation on user rating networks, item-based influence propagation on user-rating activity networks, and term-based influence propagation on user-review activity networks, respectively. We compute three kinds of social-influence-based user similarity scores based on three social-influence propagation models, respectively. Third, a unified social-influence-based CF prediction model is proposed to infer rating tastes by incorporating three kinds of social-influence-based similarity measures with different weighting factors. We design a weight-learning algorithm, SICF, to refine the prediction result by quantifying the contribution of each kind of information propagation to make a good balance between prediction accuracy and data sparsity. Extensive evaluation on real datasets demonstrates that SICF outperforms existing representative collaborative filtering methods.

References

[1]

J. C. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press.

[2]

Jacqueline Johnson Brown and Peter H. Reingen. 1987. Social ties and word-of-mouth referral behavior. J. Consumer Res. 14, 3 (1987), 350--362.

[3]

Allison June-Barlow Chaney, David M. Blei, and Tina Eliassi-Rad. 2015. A probabilistic model for using social networks in personalized item recommendation. In Proceedings of the International ACM Conference on Recommender Systems (RecSys’15). 173--180.

Digital Library

[4]

Laurent Charlin, Richard S. Zemel, and Hugo Larochelle. 2014. Leveraging user libraries to bootstrap collaborative filtering. In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’14). 173--182.

Digital Library

[5]

Hong Cheng, Yang Zhou, Xin Huang, and Jeffrey Xu Yu. 2012. Clustering large attributed information networks: An efficient incremental computing approach. Data Min. Knowl. Discov. 25, 3 (2012), 450--477.

[6]

Hong Cheng, Yang Zhou, and Jeffrey Xu Yu. 2011. Clustering large attributed graphs: A balance between structural and attribute similarities. ACM Trans. Knowl. Discov. Data 5, 2 (2011), 1--33.

Digital Library

[7]

D. Coppersmith and S. Winograd. 1990. Matrix multiplication via arithmetic progressions. J. Symbol. Comput. 9 (1990), 251--280.

Digital Library

[8]

Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, and Chong Wang. 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’14). 193--202.

Digital Library

[9]

Jacob Goldenberg, Barak Libai, and Eitan Muller. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 12, 3 (2001), 211--223.

[10]

Quanquan Gu, Charu Aggarwal, Jialu Liu, and Jiawei Han. 2013. Selective sampling on graphs for classification. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’13). 131--139.

Digital Library

[11]

Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI’15). 203--209.

[12]

Xinran He and David Kempe. 2014. Stability of influence maximization. In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’14). 1256--1265.

Digital Library

[13]

Frederick S. Hillier and Gerald J. Lieberman. 1995. Introduction to Operations Research (IBM). Mcgraw-Hill College, Blacklick, OH.

[14]

T. Hofmann. 1999. Probabilistic latent semantic indexing. In Proceedings of the International ACM SIGIR Conference Research and Development in Information Retrieval (SIGIR’99). 50--57.

Digital Library

[15]

Mohsen Jamali and Martin Ester. 2009. TrustWalker: A random walk model for combining trust-based and item-based recommendation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09). 397--406.

Digital Library

[16]

Mohsen Jamali and Martin Ester. 2009. Using a trust network to improve top-N recommendation. In Proceedings of the International ACM Conference on Recommender Systems (RecSys’09). 181--188.

Digital Library

[17]

Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the International ACM Conference on Recommender Systems (RecSys’10). 135--142.

Digital Library

[18]

Mohsen Jamali and Laks V. S. Lakshmanan. 2013. HeteroMF: Recommendation in heterogeneous information networks using context dependent factor models. In Proceedings of the 22nd International Conference on the World Wide Web (WWW’13). 643--654.

[19]

D. Kempe, J. Kleinberg, and E. Tardos. 2003. Maximizing the spread of influence through a social network. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’03). 137--146.

[20]

Risi Imre Kondor and John D. Lafferty. 2002. Diffusion kernels on graphs and other discrete input spaces. In Proceedings of the International Conference on Machine Learning (ICML’02). 315--322.

[21]

Xiangnan Kong, Philip S. Yu, Ying Ding, and David J. Wild. 2012. Meta path-based collective classification in heterogeneous information networks. In Proceedings of the International Conference on Information and Knowledge Management (CIKM’12). 1567--1571.

[22]

Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose. 2009. On social networks and collaborative recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). 195--202.

[23]

Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’08). 426--434.

Digital Library

[24]

Kisung Lee, Ling Liu, Karsten Schwan, Calton Pu, Qi Zhang, Yang Zhou, Emre Yigitoglu, and Pingpeng Yuan. 2015. Scaling iterative graph computations with GraphMap. In Proceedings of the 27th IEEE International Conference for High Performance Computing, Networking, Storage and Analysis (SC’15).

Digital Library

[25]

Xin Liu and Karl Aberer. 2013. SoCo: A social network aided context-aware recommender system. In Proceedings of the 22nd International Conference on the World Wide Web (WWW’13). 781--802.

Digital Library

[26]

Hao Ma. 2013. An experimental study on implicit social recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’13). 73--82.

Digital Library

[27]

Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’09). 203--210.

[28]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. Mining social networks using heat diffusion processes for marketing candidates selection. In Proceedings of the International ACM Conference on Information and Knowledge Management (CIKM’08). 233--242.

Digital Library

[29]

Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. SoRec: Social recommendation using probabilistic matrix factorization. In Proceedings of the International ACM Conference on Information and Knowledge Management (CIKM’08). 931--940.

Digital Library

[30]

Seth A. Myers, Chenguang Zhu, and Jure Leskovec. 2012. Information diffusion and external influence in networks. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 33--41.

Digital Library

[31]

Joseph Noel, Scott Sanner, Khoi-Nguyen Tran, Peter Christen, Lexing Xie, Edwin V. Bonilla, Ehsan Abbasnejad, and Nicolas Della Penna. 2012. New objective functions for social collaborative filtering. In Proceedings of the 21st International Conference on the World Wide Web (WWW’12). 859--868.

Digital Library

[32]

Balaji Palanisamy, Ling Liu, Yang Zhou, and Qingyang Wang. 2018. Privacy-preserving publishing of multilevel utility-controlled graph datasets. ACM Trans. Internet Technol. 18, 2 (2018), 24:1--24:21.

Digital Library

[33]

Jiaxiang Ren, Yang Zhou, Ruoming Jin, Zijie Zhang, Dejing Dou, and Pengwei Wang. 2019. Dual adversarial learning based network alignment. In Proceedings of the 19th IEEE International Conference on Data Mining (ICDM’19). 1288--1293.

[34]

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW’94). 175--186.

[35]

Everett M. Rogers. 2003. Diffusion of Innovations (5th ed.). Free Press.

[36]

Yu Rong, Xiao Wen, and Hong Cheng. 2014. A Monte Carlo algorithm for cold start recommendation. In Proceedings of the 23rd International Conference on the World Wide Web (WWW’14). 327--336.

Digital Library

[37]

Yelong Shen and Ruoming Jin. 2012. Learning personal+social latent factor model for social recommendation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12). 1303--1311.

Digital Library

[38]

Erez Shmueli, Amit Kagian, Yehuda Koren, and Ronny Lempel. 2012. Care to comment? Recommendations for commenting on news stories. In Proceedings of the 21st International Conference on the World Wide Web (WWW’12). 429--438.

Digital Library

[39]

Zhiyuan Su, Ling Liu, Mingchu Li, Xinxin Fan, and Yang Zhou. 2015. Reliable and resilient trust management in distributed service provision networks. ACM Trans. Web 9, 3 (2015), 1--37.

Digital Library

[40]

Yizhou Sun, Charu C. Aggarwal, and Jiawei Han. 2012. Relation strength-aware clustering of heterogeneous information networks with incomplete attributes. Proc. VLDB Endow. 5, 5 (2012), 394--405.

Digital Library

[41]

P. Tan, M. Steinbach, and V. Kumar. 2005. Introduction to Data Mining. Addison-Wesley, Boston, MA.

[42]

Jie Tang, Sen Wu, and Jimeng Sun. 2014. Confluence: Conformity influence in large social networks. In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’14). 347--355.

[43]

Jian Wei, Jianhua He, Kai Chen, Yi Zhou, and Zuoyin Tang. 2017. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69 (2017), 29--39.

[44]

Lei Xu, Chunxiao Jiang, Yan Chen, Yong Ren, and K. J. Ray Liu. 2019. User participation in collaborative filtering-based recommendation systems: A game theoretic approach. IEEE Trans. Cybernet. 49 (2019), 1339--1352. Issue 4.

[45]

Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, (IJCAI’17). 3203--3209.

[46]

Xiwang Yang, Harald Steck, Yang Guo, and Yong Liu. 2012. On top-k recommendation using social networks. In Proceedings of the International ACM Conference on Recommender Systems (RecSys’12). 67--74.

Digital Library

[47]

Hilmi Yildirim and Mukkai S. Krishnamoorthy. 2008. A random walk method for alleviating the sparsity problem in collaborative filtering. In Proceedings of the International ACM Conference on Recommender Systems (RecSys’08). 131--138.

[48]

Xiao Yu, Xiang Ren, Yizhou Sun, Quanquan Gu, Bradley Sturt, Urvashi Khandelwal, Brandon Norick, and Jiawei Han. 2014. Personalized entity recommendation: A heterogeneous information network approach. In Proceedings of the International Conference on Web Search and Web Data Mining (WSDM’14). 283--292.

Digital Library

[49]

C. Zhai, A. Velivelli, and B. Yu. 2004. A cross-collection mixture model for comparative text mining. In Proceedings of the ACM SIGKDD International Conference Knowledge Discovery in Databases (KDD’04). 743--748.

[50]

Chuxu Zhang, Lu Yu, Yan Wang, Chirag Shah, and Xiangliang Zhang. 2017. Collaborative user network embedding for social recommender systems. In Proceedings of the SIAM International Conference on Data Mining (SDM’17). 355--366.

[51]

Mi Zhang, Jie Tang, Xuchen Zhang, and Xiangyang Xue. 2014. Addressing cold start in recommender systems: A semi-supervised co-training algorithm. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’14). 73--82.

Digital Library

[52]

Qi Zhang, Ling Liu, Yi Ren, Kisung Lee, Yuzhe Tang, Xu Zhao, and Yang Zhou. 2013. Residency aware inter-VM communication in virtualized cloud: Performance measurement and analysis. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD’13). 204--211.

Digital Library

[53]

Ke Zhou, Shuang-Hong Yang, and Hongyuan Zha. 2011. Functional matrix factorizations for cold-start recommendation. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11). 315--324.

Digital Library

[54]

Yang Zhou. 2018. Innovative Mining, Processing, and Application of Big Graphs. Ph.D. Dissertation. Georgia Institute of Technology, Atlanta, GA.

[55]

Yang Zhou, Amnay Amimeur, Chao Jiang, Dejing Dou, Ruoming Jin, and Pengwei Wang. 2018. Density-aware local siamese autoencoder network embedding with autoencoder graph clustering. In Proceedings of the IEEE International Conference on Big Data (BigData’18). 1162--1167.

[56]

Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2009. Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2, 1 (2009), 718--729.

Digital Library

[57]

Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2010. Clustering large attributed graphs: An efficient incremental approach. In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM’10). 689--698.

Digital Library

[58]

Yang Zhou, Chao Jiang, Zijie Zhang, Dejing Dou, Ruoming Jin, and Pengwei Wang. 2019. Integrating local vertex/edge embedding via deep matrix fusion and siamese multi-label classification. In Proceedings of the IEEE International Conference on Big Data (BigData’19). 1018--1027.

[59]

Yang Zhou, Kisung Lee Ling Liu, Qi Zhang, and Balaji Palanisamy. 2019. Enhancing collaborative filtering with multi-label classification. In Proceedings of the International Conference on Computational Data and Social Networks (CSoNet’19). 323--338.

[60]

Yang Zhou and Ling Liu. 2011. Clustering analysis in large graphs with rich attributes. In Data Mining: Foundations and Intelligent Paradigms: Volume 1: Clustering, Association and Classification, Dawn E. Holmes and Lakhmi C. Jain (Eds.). Springer.

[61]

Yang Zhou and Ling Liu. 2013. Social influence based clustering of heterogeneous information networks. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’13). 338--346.

Digital Library

[62]

Yang Zhou and Ling Liu. 2014. Activity-edge centric multi-label classification for mining heterogeneous information networks. In Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’14). 1276--1285.

Digital Library

[63]

Yang Zhou and Ling Liu. 2015. Social influence based clustering and optimization over heterogeneous information networks. ACM Trans. Knowl. Discov. Data 10, 1 (2015), 1--53.

Digital Library

[64]

Yang Zhou and Ling Liu. 2019. Approximate deep network embedding for mining large-scale graphs. In Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CogMI’19). 53--60.

[65]

Yang Zhou, Ling Liu, and David Buttler. 2015. Integrating vertex-centric clustering with edge-centric clustering for meta path graph analysis. In Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’15). 1563--1572.

Digital Library

[66]

Yang Zhou, Ling Liu, Kisung Lee, Calton Pu, and Qi Zhang. 2015. Fast iterative graph computation with resource aware graph parallel abstractions. In Proceedings of the 24th ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC’15). 179--190.

Digital Library

[67]

Yang Zhou, Ling Liu, Kisung Lee, and Qi Zhang. 2015. GraphTwist: Fast iterative graph computation with two-tier optimizations. Proc. VLDB Endow. 8, 11 (2015), 1262--1273.

Digital Library

[68]

Yang Zhou, Ling Liu, Calton Pu, Xianqiang Bao, Kisung Lee, Balaji Palanisamy, Emre Yigitoglu, and Qi Zhang. 2015. Clustering service networks with entity, attribute and link heterogeneity. In Proceedings of the 22nd International Conference on Web Service (ICWS’15). 257--264.

Digital Library

[69]

Yang Zhou, Ling Liu, Sangeetha Seshadri, and Lawrence Chiu. 2016. Analyzing enterprise storage workloads with graph modeling and clustering. IEEE J. Select. Areas Commun. 34, 3 (2016), 551--574.

Digital Library

[70]

Yang Zhou, Jiaxiang Ren, Sixing Wu, Dejing Dou, Ruoming Jin, Zijie Zhang, and Pengwei Wang. 2019. Semi-supervised classification-based local vertex ranking via dual generative adversarial nets. In Proceedings of the 2019 IEEE International Conference on Big Data (BigData’19). 1267--1273.

[71]

Yang Zhou, Sixing Wu, Chao Jiang, Zijie Zhang, Dejing Dou, Ruoming Jin, and Pengwei Wang. 2018. Density-adaptive local edge representation learning with generative adversarial network multi-label edge classification. In Proceedings of the 18th IEEE International Conference on Data Mining (ICDM’18). 1464--1469.

[72]

Honglei Zhuang, Yihan Sun, Jie Tang, Jialin Zhang, and Xiaoming Sun. 2013. Influence maximization in dynamic social networks. In Proceedings of the 13th International Conference on Data Mining (ICDM’13). 1313--1318.

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Index Terms

Improving Collaborative Filtering with Social Influence over Heterogeneous Information Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Collaborative and social computing systems and tools
      1. Social networking sites
    2. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 20, Issue 4

November 2020

391 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3427795

Editor:
Ling Liu
Georgia Institute of Technology, USA

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Copyright © 2020 ACM.

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Publication History

Published: 15 October 2020

Online AM: 07 May 2020

Accepted: 01 April 2020

Revised: 01 January 2020

Received: 01 April 2018

Published in TOIT Volume 20, Issue 4

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Luo XPan CLiu ZWang LTang HZhang ZZeng D(2024)TSDRL: A three-stage deep reinforcement learning method for reliable collaboration of manufacturing service towards mass personalized productionExpert Systems with Applications10.1016/j.eswa.2024.124968257(124968)Online publication date: Dec-2024
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Jacob SMontoya JNguyen HRichardson DWarschauer M(2022)Examining the What, Why, and How of Multilingual Student Identity Development in Computer ScienceACM Transactions on Computing Education10.1145/350091822:3(1-33)Online publication date: 24-Feb-2022
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Margaris DSpiliotopoulos DVassilakis C(2021)Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative FilteringApplied Sciences10.3390/app1118836911:18(8369)Online publication date: 9-Sep-2021
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Zhou YRen JJin RZhang ZDou DYan D(2020)Unsupervised Multiple Network Alignment with Multinominal GAN and Variational Inference2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9377929(868-877)Online publication date: 10-Dec-2020
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