skip to main content
research-article

Mitigating Data Sparsity Using Similarity Reinforcement-Enhanced Collaborative Filtering

Published: 27 June 2017 Publication History

Abstract

The data sparsity problem has attracted significant attention in collaborative filtering-based recommender systems. To alleviate data sparsity, several previous efforts employed hybrid approaches that incorporate auxiliary data sources into recommendation techniques, like content, context, or social relationships. However, due to privacy and security concerns, it is generally difficult to collect such auxiliary information. In this article, we focus on the pure collaborative filtering methods without relying on any auxiliary data source. We propose an improved memory-based collaborative filtering approach enhanced by a novel similarity reinforcement mechanism. It can discover potential similarity relationships between users or items by making better use of known but limited user-item interactions, thus to extract plentiful historical rating information from similar neighbors to make more reliable and accurate rating predictions. This approach integrates user similarity reinforcement and item similarity reinforcement into a comprehensive framework and lets them enhance each other. Comprehensive experiments conducted on several public datasets demonstrate that, in the face of data sparsity, our approach achieves a significant improvement in prediction accuracy when compared with the state-of-the-art memory-based and model-based collaborative filtering algorithms.

References

[1]
Hyung Jun Ahn. 2008. A new similarity mmeasure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences 178, 1 (2008), 37--51.
[2]
Yoav Bergner, Stefan Droschler, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton, and David E. Pritchard. 2012. Model-based collaborative filtering analysis of student response data: Machine-learning item response theory. In Proceedings of the 5th International Conference on Educational Data Mining. 95--102.
[3]
John S Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 43--52.
[4]
John Canny. 2002. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 238--245.
[5]
Oscar Celma. 2010. Music Recommendation. Springer.
[6]
Wei-Sheng Chin, Yong Zhuang, Yu-Chin Juan, and Chih-Jen Lin. 2015. A fast parallel stochastic gradient method for matrix factorization in shared memory systems. ACM Transactions on Intelligent Systems and Technology (TIST) 6, 1 (2015), 2.
[7]
Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 143--177.
[8]
Sarik Ghazarian and Mohammad Ali Nematbakhsh. 2015. Enhancing memory-based collaborative filtering for group recommender systems. Expert Systems with Applications 42, 7 (2015), 3801--3812.
[9]
David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using collaborative filtering to weave an information tapestry. Communications of the ACM 35, 12 (1992), 61--70.
[10]
Miha Grčar, Dunja Mladenič, Blaž Fortuna, and Marko Grobelnik. 2005. Data Sparsity Issues in the Collaborative Filtering Framework. Springer.
[11]
Guibing Guo, Jie Zhang, and Daniel Thalmann. 2012. A simple but effective method to incorporate trusted neighbors in recommender systems. In User Modeling, Adaptation, and Personalization. Springer, 114--125.
[12]
Guibing Guo, Jie Zhang, and Daniel Thalmann. 2014. Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowledge-Based Systems 57 (2014), 57--68.
[13]
KANG Hanhoon and Seong Joon Yoo. 2007. SVM and collaborative filtering-based prediction of user preference for digital fashion recommendation systems. IEICE Transactions on Information and Systems 90, 12 (2007), 2100--2103.
[14]
Charif Haydar, Anne Boyer, and Azim Roussanaly. 2012. Hybridising collaborative filtering and trust-aware recommender systems. In Proceedings of the 8th International Conference on Web Information Systems and Technologies.
[15]
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 5--53.
[16]
Antonio Hernando, Jesús Bobadilla, and Fernando Ortega. 2016. A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowledge-Based Systems 97 (2016), 188--202.
[17]
Thomas Hofmann. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 89--115.
[18]
Mohsen Jamali and Martin Ester. 2009. Trustwalker: A random walk model for combining trust-based and item-based recommendation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 397--406.
[19]
Mohsen Jamali and Martin Ester. 2010. A matrix factorization technique with trust propagation for recommendation in social networks. In Proceedings of the 4th ACM Conference on Recommender Systems. ACM, 135--142.
[20]
Wenjun Jiang, Jie Wu, and Guojun Wang. 2015. On selecting recommenders for trust evaluation in online social networks. ACM Transactions on Internet Technology (TOIT) 15, 4 (2015), 14.
[21]
Rong Jin, Joyce Y Chai, and Luo Si. 2004. An automatic weighting scheme for collaborative filtering. In Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 337--344.
[22]
Liping Jing, Peng Wang, and Liu Yang. 2015. Sparse probabilistic matrix factorization by laplace distribution for collaborative filtering. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). 25--31.
[23]
Hamza Kaya and Ferda Nur Alpaslan. 2010. Using social networks to solve data sparsity problem in one-class collaborative filtering. In Proceedings of the 7th International Conference on Information Technology: New Generations (ITNG). IEEE, 249--252.
[24]
Ioannis Konstas, Vassilios Stathopoulos, and Joemon M. Jose. 2009. On social networks and collaborative recommendation. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 195--202.
[25]
Yehuda Koren, Robert Bell, Chris Volinsky, and others. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37.
[26]
George Lekakos and Petros Caravelas. 2008. A hybrid approach for movie recommendation. Multimedia Tools and Applications 36, 1--2 (2008), 55--70.
[27]
Haifeng Liu, Zheng Hu, Ahmad Mian, Hui Tian, and Xuzhen Zhu. 2014. A New User Similarity Model to Improve the Accuracy of Collaborative Filtering. Knowledge-Based Systems 56, 12 (2014), 156--166.
[28]
Xin Luo, Yunni Xia, and Qingsheng Zhu. 2012. Incremental collaborative filtering recommender based on regularized matrix factorization. Knowledge-Based Systems 27 (2012), 271--280.
[29]
Hao Ma, Irwin King, and Michael R. Lyu. 2007. Effective missing data prediction for collaborative filtering. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 39--46.
[30]
Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to Recommend with Social Trust Ensemble. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 203--210.
[31]
Augusto Q. Macedo, Leandro B. Marinho, and Rodrygo L. T. Santos. 2015. Context-aware event recommendation in event-based social networks. In Proceedings of the 9th ACM Conference on Recommender Systems. ACM, 123--130.
[32]
Yashar Moshfeghi, Benjamin Piwowarski, and Joemon M. Jose. 2011. Handling data sparsity in collaborative filtering using emotion and semantic based features. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 625--634.
[33]
Weike Pan, Evan Wei Xiang, Nathan Nan Liu, and Qiang Yang. 2010. Transfer Learning in Collaborative Filtering for Sparsity Reduction. In Proceedings of the 24th AAAI Conference on Artificial Intelligence, Vol. 10. 230--235.
[34]
Xochilt Ramirez-Garcia and Mario García-Valdez. 2014. Post-filtering for a restaurant context-aware recommender system. In Recent Advances on Hybrid Approaches for Designing Intelligent Systems. Springer, 695--707.
[35]
Steffen Rendle. 2012. Factorization machines with libFM. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 3 (2012), 57.
[36]
Paul Resnick and Hal R. Varian. 1997. Recommender systems. Commun. ACM 40, 3 (1997), 56--58.
[37]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to Recommender Systems Handbook. Springer.
[38]
Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. ACM, 285--295.
[39]
Marin Silic, Goran Delac, and Sinisa Srbljic. 2015. Prediction of atomic web services reliability for Qos-aware recommendation. IEEE Transactions on Services Computing 8, 3 (2015), 425--438.
[40]
Sanjeevan Sivapalan, Alireza Sadeghian, Hossein Rahnama, and Asad M. Madni. 2014. Recommender systems in e-commerce. In Proceedings of the 2014 World Automation Congress (WAC). IEEE, 179--184.
[41]
Gábor Takács, István Pilászy, Bottyán Németh, and Domonkos Tikk. 2009. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research 10, Mar (2009), 623--656.
[42]
Mingdong Tang, Wei Liang, Buqing Cao, and Xiangyun Lin. 2015. Predicting quality of cloud services for selection. International Journal of Grid and Distributed Computing 8, 4 (2015), 257--268.
[43]
Chih-Fong Tsai and Chihli Hung. 2012. Cluster ensembles in collaborative filtering recommendation. Applied Soft Computing 12, 4 (2012), 1417--1425.
[44]
Qingqing Tu and Le Dong. 2010. An intelligent personalized fashion recommendation system. In Proceedings of the 2010 International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 479--485.
[45]
Paula Cristina Vaz, David Martins de Matos, Bruno Martins, and Pavel Calado. 2012. Improving a hybrid literary book recommendation system through author ranking. In Proceedings of the 12th ACM/IEEE-CS Joint Conference on Digital Libraries. ACM, 387--388.
[46]
Slobodan Vucetic and Zoran Obradovic. 2005. Collaborative filtering using a regression-based approach. Knowledge and Information Systems 7, 1 (2005), 1--22.
[47]
Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative Deep Learning for Recommender Systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244.
[48]
Jun Wang, Arjen P. De Vries, and Marcel J. T. Reinders. 2006. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 501--508.
[49]
Jian Wu, Liang Chen, Yipeng Feng, Zibin Zheng, Meng Chu Zhou, and Zhaohui Wu. 2013. Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Transactions on Systems, Man, and Cybernetics 43, 2 (2013), 428--439.
[50]
Meng-Lun Wu, Chia-Hui Chang, and Rui-Zhe Liu. 2014. Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices. Expert Systems with Applications 41, 6 (2014), 2754--2761.
[51]
Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016a. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 153--162.
[52]
Yao Wu, Xudong Liu, Min Xie, Martin Ester, and Qing Yang. 2016b. CCCF: Improving collaborative filtering via scalable user-item co-clustering. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. ACM, 73--82.
[53]
Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013. LCARS: A location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 221--229.
[54]
Quan Yuan, Shiwan Zhao, Li Chen, Yan Liu, Shengchao Ding, Xiatian Zhang, and Wentao Zheng. 2009. Augmenting collaborative recommender by fusing explicit social relationships. In Proceedings of the Workshop on Recommender Systems and the Social Web (Recsys’09).
[55]
Yongfeng Zhang, Min Zhang, Yiqun Liu, Shaoping Ma, and Shi Feng. 2013. Localized matrix factorization for recommendation based on matrix block diagonal forms. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 1511--1520.
[56]
Zibin Zheng, Hao Ma, Michael R. Lyu, and Irwin King. 2009. WSREC: A collaborative filtering based Web service recommender system. In Proceedings of the IEEE 7th International Conference on Web Services. 437--444.
[57]
Zibin Zheng, Hao Ma, Michael R. Lyu, and Irwin King. 2011. QoS-aware Web service recommendation by collaborative filtering. IEEE Transactions on Services Computing 4, 2 (2011), 140--152.

Cited By

View all
  • (2025)From Data to Decisions: The Power of Machine Learning in Business RecommendationsIEEE Access10.1109/ACCESS.2025.353269713(17354-17397)Online publication date: 2025
  • (2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
  • (2025)Preference-based crossover technique for optimizing conflicting objectives in multi-stakeholders recommendation systemsInformation Sciences10.1016/j.ins.2024.121820700(121820)Online publication date: May-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Internet Technology
ACM Transactions on Internet Technology  Volume 17, Issue 3
Special Issue on Argumentation in Social Media and Regular Papers
August 2017
201 pages
ISSN:1533-5399
EISSN:1557-6051
DOI:10.1145/3106680
  • Editor:
  • Munindar P. Singh
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 June 2017
Accepted: 01 March 2017
Revised: 01 February 2017
Received: 01 April 2016
Published in TOIT Volume 17, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Recommender system
  2. data sparsity
  3. personalization
  4. rating prediction
  5. similarity reinforcement

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

  • National Natural Science Foundation of China

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)0
Reflects downloads up to 30 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2025)From Data to Decisions: The Power of Machine Learning in Business RecommendationsIEEE Access10.1109/ACCESS.2025.353269713(17354-17397)Online publication date: 2025
  • (2025)Addressing sparse data challenges in recommendation systems: A systematic review of rating estimation using sparse rating data and profile enrichment techniquesIntelligent Systems with Applications10.1016/j.iswa.2024.20047425(200474)Online publication date: Mar-2025
  • (2025)Preference-based crossover technique for optimizing conflicting objectives in multi-stakeholders recommendation systemsInformation Sciences10.1016/j.ins.2024.121820700(121820)Online publication date: May-2025
  • (2024)An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group PreferencesSystems10.3390/systems1211049112:11(491)Online publication date: 14-Nov-2024
  • (2024)Multi-stakeholder recommendations system with deep learning-based diversity personalization and multi-objective optimization for establishing trade-off among competing preferencesKybernetes10.1108/K-02-2024-0344Online publication date: 11-Jul-2024
  • (2024)Multi-stakeholder recommendation system through deep learning-based preference evaluation and aggregation model with multi-view information embeddingInformation Processing and Management: an International Journal10.1016/j.ipm.2024.10386261:6Online publication date: 1-Nov-2024
  • (2024)Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platformsKnowledge and Information Systems10.1007/s10115-024-02187-366:12(7799-7836)Online publication date: 1-Dec-2024
  • (2023)Movie Account Recommendation on InstagramACM Transactions on Internet Technology10.1145/357985223:1(1-21)Online publication date: 13-Jan-2023
  • (2023)Privacy-Aware Traffic Flow Prediction Based on Multi-Party Sensor Data with Zero Trust in Smart CityACM Transactions on Internet Technology10.1145/351190423:3(1-19)Online publication date: 21-Aug-2023
  • (2023)A Scalable Recommendation System with Hybrid Similarity Matrix Using Accelerated Particle Swarm Optimization2023 International Conference on Advanced Technologies for Communications (ATC)10.1109/ATC58710.2023.10318874(480-487)Online publication date: 19-Oct-2023
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media