Abstract
Collaborative Filtering (CF). the most commonly-used technique for recommender systems, does not make use of object attributes. Several hybrid recommender systems have been proposed, that aim at improving the recommendation quality by incorporating attributes in a CF model.
In this paper, we conduct an empirical study of the sensitivity of attributes for Several existing hybrid techniques using a movie dataset with an augmented movie attribute set. In addition, we propose two attribute selection measures to select informative attributes for attribute-aware CF filtering algorithms.
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References
BASILICO, J. and HOFMANN, T. (2004): Unifying collaborative and content-based filtering. In: 21st International Conference on Machine Learning. Banff. Canada.
BASU, C, HIRSH H., and COHEN, W. (1998): Recommendation as classification: Using social and content-based information in recommendation. In: 1998 Workshop on Recommender Systems. AAAI Press, Reston, Va. 11–15.
BREESE, J. S., HECKERMAN, D. and KADIE, C. (1998): Empirical analysis of predictive algorithms for collaborative filtering. In: 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). G. F. Cooper, and S. Moral, Eds. Morgan-Kaufmann, San Francisco, Calif., 43–52.
CLAYPOOL, M., GOKHALE, A. and MIRANDA T. (1999): Combining content-based and collaborative filters in an online newspaper. In: SIGIR-99 Workshop on Recommender Systems: Algorithms and Evaluation.
DELGADO, J., ISHII, N. and URA, T. (1998): Contentbased Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents. In: Cooperative Information Agents II. Learning, Mobility and Electronic Commerce for Information Discovery on the Internet. Springer-Verlag, Lecture Notes in Artificial Intelligence Series No. 1435.
DESHPANDE, M. and KARYPIS, G. (2004): Item-based top-N recommendation Algorithms. In: ACM Transactions on Information Systems 22/1, 143–177.
GOLDBERG, D., NICHOLS, D., OKI, B. M. and TERRY, D. (1992): Using collaborative filtering to weave an information tapestry. In: Commun. ACM 35, 61–70.
GOOD, N., SCHAFER, J.B., KONSTAN, J., BORCHERS, A., SARWAR, B., HERLOCKER, J., and RIEDL, J. (1999): Combining Collaborative Filtering with Personal Agents for Better Recommendations. In: 1999 Conference of the American Association of Artificial Intelligence (AAAI-99), pp 439–446.
LI, Q. and KIM, M. (2003): An Approach for Combining Content-based and Collaborative Filters. In: Sixth International Workshop on Information Retrieval with Asian Languages (ACL-2003) pp. 17–24.
MELVILLE, P., MOONEY, R. J. and NAGARAJAN, R. (2002): Content-boosted Collaborative Filtering. In: Eighteenth National Conference on Artificial Intelligence(AAAI-2002), pp. 187–192. Edmonton, Canada.
??MOVIELENS (2003): Available at http://www.grouplens.org/data.
PAZZANI, M. J. (1999).: A framework for collaborative, content-based and demographic filtering. In: Artificial Intelligence Review 13(5–6):393–408.
SARWAR, B. M., KARYPIS, G., KONSTAN, J. A. and RIEDL, J. (2000): Analysis of recommendation algorithms for E-commerce. In: 2nd ACM Conference on Electronic Commerce (EC’00). ACM, New York. 285–295.
TSO, H. L. K. and SCHMIDT-THIEME, L. (2005): Attribute-Aware Collaborative Filtering. In: 29th Annual Conference of the German Classification Society 2005, Magdeburg, Germany, (to appear)
ZIEGLER, C, SCHMIDT-THIEME, L. and LAUSEN, G. (2004): Exploiting Semantic Product Descriptions for Recommender Systems. In: 2nd ACM SIGIR Semantic Web and Information Retrieval Workshop (SWIR’04). Sheffield, UK.
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Tso, K.H.L., Schmidt-Thierne, L. (2006). Sensitivity of Attributes on the Performance of Attribute-Aware Collaborative Filtering. In: Zani, S., Cerioli, A., Riani, M., Vichi, M. (eds) Data Analysis, Classification and the Forward Search. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35978-8_32
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DOI: https://doi.org/10.1007/3-540-35978-8_32
Publisher Name: Springer, Berlin, Heidelberg
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