Collaborative and Content-based Recommender System for Social Bookmarking Website
This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical clustering, finding similar resources based on the user-s past collections by using content-based filtering, and recommending similar items to the target user. This study examines the system-s performance for the dataset collected from “del.icio.us," which is a famous social bookmarking website. Experimental results show that the proposed tag-based collaborative and content-based filtering hybridized recommender system is promising and effectiveness in the folksonomy-based bookmarking website.
[1] H.-N. Kim, A.-T. Ji, I. Ha and G.-S. Jo, "Collaborative filtering based on
collaborative tagging for enhancing the quality of recommendation,"
Electronic Commerce Research and Applications, vol. 9, Issue 1, pp.
73-83, January-February 2010.
[2] C. H. Brooks and N. Montanez, "An analysis of the effectiveness of
tagging in blogs," in: Proceedings of the 2005 AAAI Spring Symposium on
Computational Approaches to Analyzing Weblogs. Stanford, CA. March
2006.
[3] P. J. Morrison, "Tagging and searching: Search retrieval effectiveness of
folksonomies on the World Wide Web," Information Processing &
Management, vol. 44, Issue 4, pp. 1562-1579, July 2008.
[4] P. Lamere, "Social tagging and music information retrieval," Journal of
New Music Research, vol. 37, Issue 2, pp. 101-114, June 2008.
[5] G. Adomavicius and A. Tuzhilin, "Towards the next generation of
recommender systems: A survey of the state-of-the-art and possible
extensions," IEEE Transactions on Knowledge and Data Engineering,
vol. 17, no. 6, pp.734-749, 2005.
[6] A.M. Rashid, I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan
and J. Riedl, "Getting to know you: learning new user preferences in
recommender systems," in: International Conference on the Intelligent
User Interfaces, San Francisco, CA, 2002, pp.127-134.
[7] K. Wei, J. Huang and S. Fu, "A survey of e-commerce recommender
systems," in: IEEE International Conference on Service Systems and
Service Management, Chengdu, China, 2007, pp.1-5.
[8] K. Lang, "Newsweeder: Learning to filter netnews," in: Proceedings of
the Machine Learning conference, Tahoe City, CA, USA, 1995, pp.
331-339.
[9] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl,
"GroupLens: An open architecture for collaborative filtering of netnews,"
in: Proceedings of the Computer Supported Cooperative Work
Conference, Chapel Hill, NC, 1994, pp. 175-186.
[10] M. Balabanovic and Y. Shoham, "Fab: content-based collaborative
recommendation," Communications of the ACM, vol. 40, no. 3, pp.66-72,
1997.
[11] A. Mathes, "Folksonomies-cooperative classification and communication
through shared metadata," Graduate School of Library and Information
Science, University of Illinois, Urbana-Champaign, Technical Report
LIS590CMC, December 2004.
[12] R. Jäschke, A. Hotho, C. Schmitz, B. Ganter and G. Stumme,
"Discovering shared conceptualizations in folksonomies," Web
Semantics: Science, Services and Agents on the World Wide Web, Vol. 6,
No. 1, pp.38-53, February 2008.
[13] C. Marlow, M. Naaman, D. Boyd and M. Davis, "HT06, Tagging Paper,
Taxonomy, Flickr, Academic Article, ToRead." in: Proceedings of
Hypertext, New York: ACM Press, 2006.
[14] M. Memmel, M. Kockler and R. Schirru, "Providing multi source tag
recommendations in a social resource sharing platform," Journal of
Universal Computer Science, vol. 15, no. 3, pp.678-691, 2009.
[15] S.A. Golder and B. A. Huberman, "The structure of collaborative tagging
systems," Journal of Information Science, vol. 32, no. 2, pp. 198-208,
2006.
[16] J. Han and M. Kamber, Data Mining Concepts and Techniques. Morgan
Kaufmann Publishers, San Francisco, USA, 2006.
[17] G. Kowalski, Information Retrieval Systems: Theory and Implementation.
Kluwer Academic Publishers, Norwell, MA, 1997.
[18] C.-L. Huang and W.-L. Huang, "Handling sequential pattern decay:
Developing a two-stage collaborative recommender system," Electronic
Commerce Research and Applications, vol. 8, Issue 3, pp.117-129,
May-June 2009.
[1] H.-N. Kim, A.-T. Ji, I. Ha and G.-S. Jo, "Collaborative filtering based on
collaborative tagging for enhancing the quality of recommendation,"
Electronic Commerce Research and Applications, vol. 9, Issue 1, pp.
73-83, January-February 2010.
[2] C. H. Brooks and N. Montanez, "An analysis of the effectiveness of
tagging in blogs," in: Proceedings of the 2005 AAAI Spring Symposium on
Computational Approaches to Analyzing Weblogs. Stanford, CA. March
2006.
[3] P. J. Morrison, "Tagging and searching: Search retrieval effectiveness of
folksonomies on the World Wide Web," Information Processing &
Management, vol. 44, Issue 4, pp. 1562-1579, July 2008.
[4] P. Lamere, "Social tagging and music information retrieval," Journal of
New Music Research, vol. 37, Issue 2, pp. 101-114, June 2008.
[5] G. Adomavicius and A. Tuzhilin, "Towards the next generation of
recommender systems: A survey of the state-of-the-art and possible
extensions," IEEE Transactions on Knowledge and Data Engineering,
vol. 17, no. 6, pp.734-749, 2005.
[6] A.M. Rashid, I. Albert, D. Cosley, S.K. Lam, S.M. McNee, J.A. Konstan
and J. Riedl, "Getting to know you: learning new user preferences in
recommender systems," in: International Conference on the Intelligent
User Interfaces, San Francisco, CA, 2002, pp.127-134.
[7] K. Wei, J. Huang and S. Fu, "A survey of e-commerce recommender
systems," in: IEEE International Conference on Service Systems and
Service Management, Chengdu, China, 2007, pp.1-5.
[8] K. Lang, "Newsweeder: Learning to filter netnews," in: Proceedings of
the Machine Learning conference, Tahoe City, CA, USA, 1995, pp.
331-339.
[9] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl,
"GroupLens: An open architecture for collaborative filtering of netnews,"
in: Proceedings of the Computer Supported Cooperative Work
Conference, Chapel Hill, NC, 1994, pp. 175-186.
[10] M. Balabanovic and Y. Shoham, "Fab: content-based collaborative
recommendation," Communications of the ACM, vol. 40, no. 3, pp.66-72,
1997.
[11] A. Mathes, "Folksonomies-cooperative classification and communication
through shared metadata," Graduate School of Library and Information
Science, University of Illinois, Urbana-Champaign, Technical Report
LIS590CMC, December 2004.
[12] R. Jäschke, A. Hotho, C. Schmitz, B. Ganter and G. Stumme,
"Discovering shared conceptualizations in folksonomies," Web
Semantics: Science, Services and Agents on the World Wide Web, Vol. 6,
No. 1, pp.38-53, February 2008.
[13] C. Marlow, M. Naaman, D. Boyd and M. Davis, "HT06, Tagging Paper,
Taxonomy, Flickr, Academic Article, ToRead." in: Proceedings of
Hypertext, New York: ACM Press, 2006.
[14] M. Memmel, M. Kockler and R. Schirru, "Providing multi source tag
recommendations in a social resource sharing platform," Journal of
Universal Computer Science, vol. 15, no. 3, pp.678-691, 2009.
[15] S.A. Golder and B. A. Huberman, "The structure of collaborative tagging
systems," Journal of Information Science, vol. 32, no. 2, pp. 198-208,
2006.
[16] J. Han and M. Kamber, Data Mining Concepts and Techniques. Morgan
Kaufmann Publishers, San Francisco, USA, 2006.
[17] G. Kowalski, Information Retrieval Systems: Theory and Implementation.
Kluwer Academic Publishers, Norwell, MA, 1997.
[18] C.-L. Huang and W.-L. Huang, "Handling sequential pattern decay:
Developing a two-stage collaborative recommender system," Electronic
Commerce Research and Applications, vol. 8, Issue 3, pp.117-129,
May-June 2009.
@article{"International Journal of Information, Control and Computer Sciences:63256", author = "Cheng-Lung Huang and Cheng-Wei Lin", title = "Collaborative and Content-based Recommender System for Social Bookmarking Website", abstract = "This study proposes a new recommender system based on the collaborative folksonomy. The purpose of the proposed system is to recommend Internet resources (such as books, articles, documents, pictures, audio and video) to users. The proposed method includes four steps: creating the user profile based on the tags, grouping the similar users into clusters using an agglomerative hierarchical clustering, finding similar resources based on the user-s past collections by using content-based filtering, and recommending similar items to the target user. This study examines the system-s performance for the dataset collected from “del.icio.us," which is a famous social bookmarking website. Experimental results show that the proposed tag-based collaborative and content-based filtering hybridized recommender system is promising and effectiveness in the folksonomy-based bookmarking website.
", keywords = "Collaborative recommendation, Folksonomy, Social tagging", volume = "4", number = "8", pages = "1360-6", }
