Abstract
Content-based filtering is a popular framework for item recommendation. Typical methods determine items to be recommended by measuring the similarity between items based on the tags provided by users. However, because the usefulness of tags depends on the annotator’s skills, vocabulary and feelings, many tags are irrelevant. This fact degrades the accuracy of simple content-based recommendation methods. To tackle this issue, this paper enhances content-based filtering by introducing the idea of tag ranking, a state-of-the-art framework that ranks tags according to their relevance levels. We conduct experiments on videos from a video-sharing site. The results show that tag ranking significantly improves item recommendation performance, despite its simplicity.
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References
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms. In: 10th International Conference on World Wide Web (WWW), pp.285–295 (2001)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: 14th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 43–52 (1998)
Maltz, D., Ehrlich, K.: Pointing the Way: Active Collaborative Filtering. In: SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 202–209 (1995)
Markines, B., Cattuto, C., Menczer, F., Benz, D., Hotho, A., Stumme, G.: Evaluating Similarity Measures for Emergent Semantics of Social Tagging. In: 18th International Conference on World Wide Web (WWW), pp. 641–650 (2009)
Golder, S., Huberman, B.A.: Usage patterns of collaborative tagging systems. Journal of Information Science 32(2), 198–208 (2006)
Liu, D., Hua, X.-S., Yang, L., Wang, M., Zhang, H.-J.: Tag Ranking. In: 18th International Conference on World Wide Web (WWW), pp. 351–360 (2009)
Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley (1989)
Sigurbjörnsson, B., Zwol, R.V.: Flickr tag recommendation based on collective knowledge. In: 17th International Conference on World Wide Web (WWW), pp. 327–336 (2008)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Yates, R.-B., Neto, B.-R.: Modern Information Retrieval. Addison Wesley (1999)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining Content-Based and Collaborative Filters in an Online Newspaper. In: ACM SIGIR Workshop on Recommender Systems (1999)
Pazzani, M.: A Framework for Collaborative, Content-Based, and Demographic Filtering. Artificial Intelligence Review, 393–408 (1999)
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© 2012 Springer-Verlag Berlin Heidelberg
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Yoshida, T., Irie, G., Satou, T., Kojima, A., Higashino, S. (2012). Improving Item Recommendation Based on Social Tag Ranking. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_17
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DOI: https://doi.org/10.1007/978-3-642-27355-1_17
Publisher Name: Springer, Berlin, Heidelberg
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