research-article

A Random Walk Model for Item Recommendation in Social Tagging Systems

Authors:

Daniel D. Zeng,

Xiaolong ZhengAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 4, Issue 2

Article No.: 8, Pages 1 - 24

https://doi.org/10.1145/2490860

Published: 01 August 2013 Publication History

Abstract

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.

References

[1]

Adomavicius, G. and Tuzhilin, A. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6, 734--749.

Digital Library

[2]

Adomavicius, G. and Zhang, J. 2012. Impact of data characteristics on recommender systems performance. ACM Trans. Manage. Inf. Syst. 3, 1, 1--17.

Digital Library

[3]

Balabanović, M. and Shoham, Y. 1997. Fab: Content-based, collaborative recommendation. Commun. ACM 40, 3, 66--72.

Digital Library

[4]

Basu, C., Hirsh, H., and Cohen, W. 1998. Recommendation as classification: Using social and content-based information in recommendation. In Proceedings of the National Conference on Artificial Intelligence. 714--720.

Digital Library

[5]

Bellogin, A., Castells, P., and Cantador, I. 2011. Self-adjusting hybrid recommenders based on social network analysis. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1147--1148.

Digital Library

[6]

Bogers, T. 2010. Movie recommendation using random walks over the contextual graph. In Proceedings of the 2nd Workshop on Context-Aware Recommender Systems.

[7]

Breese, J. S., Heckerman, D., and Kadie, C. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 43--52.

Digital Library

[8]

Burke, R. 2002. Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12, 4, 331--370.

Digital Library

[9]

Cai, Y., Zhang, M., Luo, D., Ding, C., and Chakravarthy, S. 2011. Low-order tensor decompositions for social tagging recommendation. In Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 695--704.

Digital Library

[10]

Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., and Sartin, M. 1999. Combining content-based and collaborative filters in an online newspaper. In Proceedings of ACM SIGIR Workshop on Recommender Systems.

[11]

Deshpande, M. and Karypis, G. 2004. Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22, 1, 143--177.

Digital Library

[12]

Fouss, F., Yen, L., Pirotte, A., and Saerens, M. 2006. An experimental investigation of graph kernels on a collaborative recommendation task. In Proceedings of the IEEE International Conference on Data Mining. 863--868.

Digital Library

[13]

Fouss, F., Pirotte, A., Renders, J. M., and Saerens, M. 2007. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19, 3, 355--369.

Digital Library

[14]

Ghazanfar, M. A. and Prugel-Bennett, A. 2010. A scalable, accurate hybrid recommender system. In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining. 94--98.

Digital Library

[15]

Goldberg, D., Nichols, D., Oki, B. M., and Terry, D. 1992. Using collaborative filtering to weave an information tapestry. Commun. ACM 35, 12, 61--70.

Digital Library

[16]

Good, N., Schafer, J. B., Konstan, J. A., Borchers, A., Sarwar, B., Herlocker, J., and Riedl, J. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proceedings of the National Conference on Artificial Intelligence. 439--446.

Digital Library

[17]

Gori, M. and Pucci, A. 2007. ItemRank: A random-walk based scoring algorithm for recommender engines. In Proceedings of the 20th International Joint Conference on Artifical Intelligence. 2766--2771.

Digital Library

[18]

Guan, Z., Wang, C., Bu, J., Chen, C., Yang, K., Cai, D., and He, X. 2010. Document recommendation in social tagging services. In Proceedings of the 19th International Conference on World Wide Web. 391--400.

Digital Library

[19]

Gunawardana, A. and Meek, C. 2009. A unified approach to building hybrid recommender systems. In Proceedings of the 3rd ACM Conference on Recommender Systems. 117--124.

Digital Library

[20]

Guy, I., Zwerdling, N., Ronen, I., Carmel, D., and Uziel, E. 2010. Social media recommendation based on people and tags. In Proceedings of the 33rd International ACM SIGIR conference on Research and Development in Information Retrieval. 194--201.

Digital Library

[21]

Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1, 5--53.

Digital Library

[22]

Hofmann, T. 2003. Collaborative filtering via gaussian probabilistic latent semantic analysis. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval. 259--266.

Digital Library

[23]

Hofmann, T. 2004. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22, 1, 89--115.

Digital Library

[24]

Hotho, A., J Schke, R., Schmitz, C., and Stumme, G. 2006. Information retrieval in folksonomies: Search and ranking. In Proceedings of the 3rd European Conference on the Semantic Web: Research and Applications. 411--426.

Digital Library

[25]

Huang, Z., Chen, H., and Zeng, D. 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22, 1, 116--142.

Digital Library

[26]

Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., and Stumme, G 2008. Tag recommendations in social bookmarking systems. AI Commun. 21, 4, 231--247.

Digital Library

[27]

Jamali, M. and Ester, M. 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. 397--406.

Digital Library

[28]

Jeon, W., Cho, S., Cha, J., and Byun, H. 2011. Tag suggestion using visual content and social tag. In Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication. 1--5.

Digital Library

[29]

Jiang, B., Ling, Y., and Wang, J. 2010. Tag Recommendation Based on Social Comment Network. J. Digital Content Technol. Appli. 4, 8, 110--117.

[30]

Jin, R., Chai, J. Y., and Si, L. 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.

Digital Library

[31]

Konstas, I., Stathopoulos, V., and Jose, J. M. 2009. On social networks and collaborative recommendation. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 195--202.

Digital Library

[32]

Li, X., Snoek, C. G. M., and Worring, M. 2008. Learning tag relevance by neighbor voting for social image retrieval. In Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval. 180--187.

Digital Library

[33]

Linden, G., Smith, B., and York, J. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 7, 1, 76--80.

Digital Library

[34]

Liu, K., Fang, B., and Zhang, W. 2010. Speak the same language with your friends: Augmenting tag recommenders with social relations. In Proceedings of the 21st ACM Conference on Hypertext and Hypermedia. 45--50.

Digital Library

[35]

Nanopoulos, A., Rafailidis, D., Symeonidis, P., and Manolopoulos, Y. 2010. MusicBox: Personalized music recommendation based on cubic analysis of social tags. Trans. Audio, Speech and Lang. Proc. 18, 2, 407--412.

Digital Library

[36]

Ma, H., King, I., and Lyu, M. R. 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. 39--46.

Digital Library

[37]

Ma, H., Zhou, T. C., Lyu, M. R., and King, I. 2011. Improving recommender systems by incorporating social contextual information. ACM Trans. Inf. Syst. 29, 2, 9.

Digital Library

[38]

Marinho, L. B., Hotho, A., J Schke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., and Symeonidis, P. 2012. Recommender Systems for Social Tagging Systems. Springer, New York.

Digital Library

[39]

Page, L., Brin, S., Motwani, R., and Winograd, T. 1999. The PageRank citation ranking: Bringing order to the web. Tech. rep. SIDL-WP-1999-0120, Standford Info Lab.

[40]

Pazzani, M. J. 1999. A framework for collaborative, content-based and demographic filtering. Artif. Intell. Rev. 13, 5, 393--408.

Digital Library

[41]

Peng, J., Zeng, D., Liu, B., and Zhao, H. 2010a. CFUI: Collaborative filtering with unlabeled items. In Proceedings of the 20th Workshop on Information Technologies and Systems.

[42]

Peng, J., Zeng, D. D., Zhao, H. and Wang, F. 2010b. Collaborative filtering in social tagging systems based on joint item-tag recommendations. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 809--818.

Digital Library

[43]

Peng, J., Zeng, D. D., and Huang, Z. 2011. Latent subject-centered modeling of collaborative tagging: An application in social search. ACM Trans. Manage. Inf. Syst. 2, 3, 1--23.

Digital Library

[44]

Popescul, A., Pennock, D. M., and Lawrence, S. 2001. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence. 437--444.

Digital Library

[45]

Rendle, S., Balby Marinho, L., Nanopoulos, A., and Schmidt-Thieme, L. 2009. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 727--736.

Digital Library

[46]

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

Digital Library

[47]

Salakhutdinov, R. and Mnih, A. 2008. Probabilistic matrix factorization. Adv. Neural Inf. Proc. Syst. 20. 1257--1264.

[48]

Sarwar, B., Karypis, G., Konstan, J., and Reidl, J. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. 285--295.

Digital Library

[49]

Si, L. and Jin, R. 2003. Flexible mixture model for collaborative filtering. In Proceedings of the 20th International Conference on Machine Learning (ICML’03).

[50]

Soboroff, I. and Nicholas, C. 1999. Combining content and collaboration in text filtering. In Proceedings of the International Joint Conference on Artificial Intelligence Workshop: Machine Learning for Information Filtering.

[51]

Su, X. and Khoshgoftaar, T. M. 2009. A survey of collaborative filtering techniques. Adv. Artif. Intell. 2, 2.

Digital Library

[52]

Symeonidis, P., Nanopoulos, A., and Manolopoulos, Y. 2010. A unified framework for providing recommendations in social tagging systems based on ternary semantic analysis. IEEE Trans. Knowl. Data Eng. 22, 2, 179--192.

Digital Library

[53]

Tso-Sutter, K. H. L., Marinho, L. B., and Schmidt-Thieme, L. 2008. Tag-aware recommender systems by fusion of collaborative filtering algorithms. In Proceedings of the ACM Symposium on Applied Computing. 1995--1999.

Digital Library

[54]

Vipul, V. 2012. Hybrid recommender systems: Content-boosted collaborative filtering for improved recommendations. In Proceedings of the International Conference on Communication Systems and Network Technologies. 649--653.

Digital Library

[55]

Wang, J., De Vries, A. P., and Reinders, M. J. T. 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. 501--508.

Digital Library

[56]

Wei, W. and Ram, S. 2012. Using a network analysis approach for organizing social bookmarking tags and enabling web content discovery. ACM Trans. Manage. Inf. Syst. 3, 3, 1--16.

Digital Library

[57]

Wetzker, R., Umbrath, W., and Said, A. 2009. A hybrid approach to item recommendation in folksonomies. In Proceedings of the Workshop on Exploiting Semantic Annotations in Information Retrieval (WSDM’09). 25--29.

Digital Library

[58]

Yildirim, H. and Krishnamoorthy, M. S. 2008. A random walk method for alleviating the sparsity problem in collaborative filtering. In Proceedings of the ACM Conference on Recommender Systems. 131--138.

Digital Library

[59]

Zeng, D. and Li, H. 2008. How useful are tags?---An empirical analysis of collaborative tagging for Web page recommendation. In Proceedings of the IEEE ISI PAISI, PACCF, and SOCO International Workshops on Intelligence and Security Informatics. 320--330.

Digital Library

[60]

Zhang, Z. K., Zhou, T., and Zhang, Y. C. 2010. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A: Stat. Mech. Its Appl. 389, 1, 179--186.

[61]

Zhao, S., Du, N., Nauerz, A., Zhang, X., Yuan, Q., and Fu, R. 2008. Improved recommendation based on collaborative tagging behaviors. In Proceedings of the 13th International Conference on Intelligent User Interfaces, 413--416.

Digital Library

[62]

Zhen, Y., Li, W. J., and Yeung, D. Y. 2009. TagiCoFi: Tag informed collaborative filtering. In Proceedings of the 3rd ACM Conference on Recommender Systems. 69--76.

Digital Library

[63]

Zheng, N. and Li, Q. 2011. A recommender system based on tag and time information for social tagging systems. Expert Syst. Appl. 38, 4, 4575--4587.

Digital Library

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Index Terms

A Random Walk Model for Item Recommendation in Social Tagging Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 4, Issue 2

August 2013

113 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2499962

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Copyright © 2013 ACM.

© 2013 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Publication History

Published: 01 August 2013

Accepted: 01 May 2013

Revised: 01 March 2013

Received: 01 April 2012

Published in TMIS Volume 4, Issue 2

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Cited By

Gangadharan KPurandaran AMalathi KSubramanian BJeyaraj RJung S(2025)From Data to Decisions: The Power of Machine Learning in Business RecommendationsIEEE Access10.1109/ACCESS.2025.353269713(17354-17397)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3532697
Xu JWang JLu T(2024)A User Purchase Motivation-Aware Recommender SystemSSRN Electronic Journal10.2139/ssrn.4765844Online publication date: 2024
https://doi.org/10.2139/ssrn.4765844
Wu ZDai XWang XXiong YGao SLiu D(2023)A Multi-Label Recommendation Algorithm Based on Graph Attention and Sentiment Correction2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)10.1109/AINIT59027.2023.10212701(396-401)Online publication date: 16-Jun-2023
https://doi.org/10.1109/AINIT59027.2023.10212701
Wu YHuang Wayne WYu’d LTiand Y(2023)Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNsIEEE Access10.1109/ACCESS.2022.322459811(1854-1866)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2022.3224598
Puri GVaranasi ASingh GAgarwal HTomar RChoudhury T(2023)Investigating Graph-Based Recommendations Systems and Graph Traversal AlgorithmsMicro-Electronics and Telecommunication Engineering10.1007/978-981-19-9512-5_11(115-126)Online publication date: 2-Jun-2023
https://doi.org/10.1007/978-981-19-9512-5_11
Pande CWitschel HMartin A(2022)New Hybrid Techniques for Business Recommender SystemsApplied Sciences10.3390/app1210480412:10(4804)Online publication date: 10-May-2022
https://doi.org/10.3390/app12104804
Pan XZeng XDing L(2022)Topic optimization–incorporated collaborative recommendation for social taggingData Technologies and Applications10.1108/DTA-11-2021-033258:3(407-426)Online publication date: 9-Dec-2022
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Maheswari MGeetha SKumar SKaruppiah MSamanta DPark Y(2021)PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile ApplicationsIEEE Access10.1109/ACCESS.2021.30535839(20819-20827)Online publication date: 2021
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Park HOh K(2021)A Path-Based Personalized Recommendation Using Q LearningAdvances in Artificial Intelligence and Applied Cognitive Computing10.1007/978-3-030-70296-0_25(323-333)Online publication date: 15-Oct-2021
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