Abstract
In this paper, we propose a Topical PageRank based algorithm for recommender systems, which ranks products by analyzing previous user-item relationships, and recommends top-rank items to potentially interested users. In order to rank all the items for each particular user, we attempt to establish a correlation graph among items, and implement ranking process with our algorithm. We evaluate our algorithm on MovieLens dataset and empirical experiments demonstrate that it outperforms other state-of-the-art recommending algorithms.
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
Fouss, F., Pirotte, A., Saerens, M.: A novel way of computing similarities between nodes of a graph, with application to collaborative recommendation. In: IEEE/WIC/ACM International Joint Conference on Web Intelligence, pp. 550–556 (2005)
Nie, L., Davison, B.D., Qi, X.: Topical link analysis for web search. In: ACM SIGIR Conference on Information Retrieval, pp. 91–98 (2006)
Gori, M., Pucci, A.: ItemRank: A Random-Walk Based Scoring Algorithm for Recommender Engines. In: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 2766–2771 (2007)
Gori, M., Pucci, A.: Research paper recommender systems: A random-walk based approach. In: IEEE/WIC/ACM International Joint Conference onWeb Intelligence, pp. 778–781 (2006)
Adomavicius, G., Tuzhilin, A.: Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. In: IEEE Transactions on Knowledge and Data Engineering, pp. 634–749 (2005)
Kim, D., Yum, B.: Collaborative Filtering Based on Iterative Principal Component Analysis. In: Expert Systems with Applications, pp. 823–830 (2005)
Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion. In: 29th ACM SIGIR Conference on Information Retrieval, pp. 501–508 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, L., Li, C. (2008). A Novel Recommending Algorithm Based on Topical PageRank. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_45
Download citation
DOI: https://doi.org/10.1007/978-3-540-89378-3_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89377-6
Online ISBN: 978-3-540-89378-3
eBook Packages: Computer ScienceComputer Science (R0)