Abstract
This paper discusses accuracy in processing ratings of and recommendations for item features. Such processing facilitates feature-based user navigation in recommender system interfaces. Item features, often in the form of tags, categories or meta-data, are becoming important hypertext components of recommender interfaces. Recommending features would help unfamiliar users navigate in such environments. This work explores techniques for improving feature recommendation accuracy. Conversely, it also examines possibilities for processing user ratings of features to improve recommendation of both features and items.
This work’s illustrative implementation is a web portal for a museum collection that lets users browse, rate and receive recommendations for both artworks and interrelated topics about them. Accuracy measurements compare proposed techniques for processing feature ratings and recommending features. Resulting techniques recommend features with relative accuracy. Analysis indicates that processing ratings of either features or items does not improve accuracy of recommending the other.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B.M., Herlocker, J.L., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI 1999), Orlando, Florida, USA, July 18-22, 1999, pp. 439–446 (1999)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing 7(1), 76–80 (2003)
Aroyo, L., Brussee, R., Rutledge, L., Gorgels, P., Stash, N., Wang, Y.: Personalized museum experience: the Rijksmuseum use case. In: Museums and the Web 2007, San Francisco, USA, April 11-14 (2007)
Wang, Y., Aroyo, L., Stash, N., Rutledge, L.: Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study. In: Proceedings of User Modeling 2007, Corfu, Greece, June 2007, pp. 385–389 (2007)
Sen, S., Lam, S.K., Rashid, A.M., Cosley, D., Frankowski, D., Osterhouse, J., Harper, F.M., Riedl, J.: Tagging, communities, vocabulary, evolution. In: CSCW 2006: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pp. 181–190. ACM, New York (2006)
Heath, T., Motta, E.: Revyu.com: A reviewing and rating site for the web of data. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ISWC 2007. LNCS, vol. 4825, pp. 895–902. Springer, Heidelberg (2007)
Schreiber, G., Amin, A., van Assem, M., de Boer, V., Hardman, L., Hildebrand, M., Hollink, L., Huang, Z., van Kersen, J., de Niet, M., Omelayenko, B., van Ossenbruggen, J., Siebes, R., Taekema, J., Wielemaker, J., Wielinga, B.J.: Multimedian e-culture demonstrator. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 951–958. Springer, Heidelberg (2006)
Cramer, H., Wielinga, B., Ramlal, S., Evers, V., Rutledge, L., Stash, N.: The effects of transparency on perceived and actual competence of a content-based recommender. In: CHI 2008 Semantic Web User Interaction Workshop (SWUI 2008), Florence, Italy, April 5 (2008)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230–237. ACM Press, New York (1999)
Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 355–359. Springer, Heidelberg (2007)
Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rutledge, L., Stash, N., Wang, Y., Aroyo, L. (2008). Accuracy in Rating and Recommending Item Features. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds) Adaptive Hypermedia and Adaptive Web-Based Systems. AH 2008. Lecture Notes in Computer Science, vol 5149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70987-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-70987-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70984-8
Online ISBN: 978-3-540-70987-9
eBook Packages: Computer ScienceComputer Science (R0)