Personalized Tag Recommendation Based on Transfer Matrix and Collaborative Filtering

Shaowu Zhang^1,2, Yanyan Ge¹

¹School of Computer Science and Technology, Dalian University of Technology, Dalian, China.
²School of Computer Science and Engineering, Xinjiang University of Finance and Economics, Urumqi, China.

DOI: 10.4236/jcc.2015.39002                

Abstract

In social tagging systems, users are allowed to label resources with tags, and thus the system builds a personalized tag vocabulary for every user based on their distinct preferences. In order to make the best of the personalized characteristic of users’ tagging behavior, firstly the transfer matrix is used in this paper, and the tag distributions of query resources are mapped to users’ query before the recommendation. Meanwhile, we find that only considering the user’s preference model, the method cannot recommend new tags for users. So we utilize the thought of collaborative filtering, and produce the recommend tags based on the query user and his/her nearest neighbors' preference models. The experiments conducted on the Delicious corpus show that our method combining transfer matrix with collaborative filtering produces better recommendation results.

Keywords

Tag Recommendation, Collaborative Filtering, Transfer Tensor

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L. and Stumme, G. (2007) Tag Recommendations in Folksonomies. Knowledge Discovery in Databases: PKDD 2007, Springer, Berlin, 506-514. http://dx.doi.org/10.1007/978-3-540-74976-9_52
[2]	Wetzker, R., Zimmermann, C., Bauckhage, C. and Albayrak, S. (2010) I Tag, You Tag: Translating Tags for Advanced User Models. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York, 2010, 71-80. http://dx.doi.org/10.1145/1718487.1718497
[3]	Zhang, N., Zhang, Y. and Tang, J. (2009) A Tag Recommendation System for Folksonomy. Proceedings of the 2nd ACM Workshop on Social Web Search and Mining, New York, 2 November 2009, 9-16.http://dx.doi.org/10.1145/1651437.1651440
[4]	Guan, Z., Bu, J., Mei, Q., Chen, C. and Wang, C. (2009) Personalized Tag Recommendation Using Graph-Based Ranking on Multi-Type Interrelated Objects. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, 19-23 July 2009, 540-547.http://dx.doi.org/10.1145/1571941.1572034
[5]	Symeonidis, P., Nanopoulos, A. and Manolopoulos, Y. (2008) Tag Recommendations Based on Tensor Dimensionality Reduction. Proceedings of the 2008 ACM Conference on Recommender Systems, New York, 23-25 October 2008, 43-50. http://dx.doi.org/10.1145/1454008.1454017
[6]	Rendle, S., Balby Marinho, L., Nanopoulos, A. and Schmidt-Thieme, L. (2009) Learning Optimal Ranking with Tensor Factorization for Tag Recommendation. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2009, 727-736. http://dx.doi.org/10.1145/1557019.1557100
[7]	Rendle, S. and Schmidt-Thieme, L. (2010) Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, New York, 2010, 81-90. http://dx.doi.org/10.1145/1718487.1718498
[8]	Hotho, A., Jäschke, R., Schmitz, C. and Stumme, G. (2006) Bibsonomy: A Social Bookmark and Publication Sharing System. Proceedings of the Conceptual Structures Tool Interoperability Workshop at the 14th International Conference on Conceptual Structures, Aalborg, 2006, 87-102.
[9]	Hotho, A., Jäschke, R., Schmitz, C. and Stumme, G. (2006) Folkrank: A Ranking Algorithm for Folksonomies. Vol. 1, In: Althoff, K.D., Ed., Klaus-Dieter Althoff, LWA, Hildesheim, 111-114.
[10]	Hotho, A., Jäschke, R., Schmitz, C. and Stumme, G. (2006) Information Retrieval in Folksonomies: Search and Ranking. Springer, Berlin, 411-426. http://dx.doi.org/10.1007/11762256_31
[11]	Sigurbjörnsson, B. and Van Zwol, R. (2008) Flickr Tag Recommendation Based on Collective Knowledge. Proceedings of the 17th International Conference on World Wide Web, Beijing, 21-25 April 2008, 327-336.http://dx.doi.org/10.1145/1367497.1367542
[12]	Chen, W.Y., Zhang, D. and Chang, E.Y. (2008) Combinational Collaborative Filtering for Personalized Community Recommendation. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, 24-27 August 2008, 115-123. http://dx.doi.org/10.1145/1401890.1401909
[13]	Golder, S.A. and Huberman, B.A. (2006) Usage Patterns of Collaborative Tagging Systems. Journal of Information Science, 32, 198-208. http://dx.doi.org/10.1177/0165551506062337
[14]	Marlow, C., Naaman, M., Boyd, D. and Davis, M. (2006) HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to Read. Proceedings of the Seventeenth Conference on Hypertext and Hypermedia, Odense, 22-25 August 2006, 31- 40. http://dx.doi.org/10.1145/1149941.1149949
[15]	Mika, P. (2005) Ontologies Are Us: A Unified Model of Social Networks and Semantics. In: Gil, Y., Motta, E., Benjamins, V.R. and Musen, M.A., Eds., The Semantic Web-ISWC 2005, Springer, Berlin Heidelberg, 522-536. http://dx.doi.org/10.1007/11574620_38
[16]	Marinho, L.B. and Schmidt-Thieme, L. (2008) Collaborative Tag Recommendations. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L. and Decker, R., Eds., Data Analysis, Machine Learning and Applications, Springer, Berlin Heidelberg, 533-540. http://dx.doi.org/10.1007/978-3-540-78246-9_63
[17]	Xu, Z., Fu, Y., Mao, J. and Su, D. (2006) Towards the Semantic Web: Collaborative Tag Suggestions. Proceedings of the Collaborative Web Tagging Workshop at WWW, Edinburgh, 23-26 May 2006, 1-8.
[18]	Koren, Y. (2010) Collaborative Filtering with Temporal Dynamics. Communications of the ACM, 53, 89-97.http://dx.doi.org/10.1145/1721654.1721677
[19]	Gueye, M., Abdessalem, T. and Naacke, H. (2014) A Parameter-Free Algorithm for an Optimized Tag Recommendation List Size. Proceedings of the 8th ACM Conference on Recommender Systems, ACM Press, New York, 233-240.
[20]	Gueye, M., Abdessalem, T. and Naacke, H. (2013) An Efficient Trust-and Popularity-Based Tag Recommender.
[21]	Ifada, N. and Nayak, R. (2015) Do-Rank: DCG Optimization for Learning-to-Rank in Tag-Based Item Recommendation Systems. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D. and Motoda, H., Eds., Advances in Knowledge Discovery and Data Mining, Springer International Publishing, Berlin Heidelberg, 510-521. http://dx.doi.org/10.1007/978-3-319-18032-8_40
[22]	Wetzker, R., Zimmermann, C. and Bauckhage, C. (2008) Analyzing Social Bookmarking Systems: A Delicious Cookbook. Proceedings of the ECAI 2008 Mining Social Data Workshop, IOS Press, Amsterdam, 26-30.