Abstract
User-based Collaborative Filtering (CF) systems generate recommendations for a specific user by combining feedback (i.e. information about what is relevant for a user) provided by a set of people similar to that user. In these system the similarity among people is computed by taking into account the set of shared resources. However, there are several application domains, such as social tagging systems, where each user may have several different Topic of Interests (ToIs). In these cases, two users could share only some interests and, therefore, only a part of the feedback should be considered for producing recommendations. Focusing on social tagging systems, we propose here a novel approach to detect ToIs in the collection of the bookmarks of a user. Given a specific ToI, we adaptively identify similar people (i.e., sharing the same ToI) and select only the resources relevant to the specific ToI.
This is a preview of subscription content, log in via an institution to check access.
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
Zanardi, V., Capra, L.: Social ranking: Finding relevant content in web 2.0. In: Proc. of the 2nd ACM Int. Conf. on Recommender Systems, Lausanne, Switzerland (2008)
Cantador, I., Szomszor, M., Alani, H., Fernández, M., Castells, P.: Enriching Ontological User Profiles with Tagging History for Multi-Domain Recommendations. In: Proceedings of the 1st International Workshop on Collective Semantics: Collective Intelligence and the Semantic Web (CISWeb 2008), pp. 5–19 (2008)
Dattolo, A., Ferrara, F., Tasso, C.: On social semantic relations for recommending tags and resources using folksonomies. In: Human-Computer Systems Interaction. Backgrounds and Applications 2
Dattolo, A., Ferrara, F., Tasso, C.: Supporting Personalized user Concept Spaces and Recommendations for a Publication Sharing System. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 325–330. Springer, Heidelberg (2009)
Zhou, T., Ma, H., Lyu, M., King, I.: Userrec: A user recommendation framework in social tagging systems. In: Proc. of the 24th AAAI Conf., Atlanta, Geogia, USA, pp. 1486–1491 (2010)
Baltrunas, L., Ricci, F.: Locally Adaptive Neighborhood Selection for Collaborative Filtering Recommendations. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 22–31. Springer, Heidelberg (2008)
Lancichinetti, A., Fortunato, S., Kertész, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11 (2009)
Opsahl, T., Agneessens, F., Skvoretz, J.: Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks 32 (2010)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative Filtering Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007)
Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. Transaction on Information Systems 1, 143–177 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferrara, F., Tasso, C. (2011). Improving Collaborative Filtering in Social Tagging Systems. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-25274-7_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
eBook Packages: Computer ScienceComputer Science (R0)