Abstract
Recommender systems are a class of personalized systems that aim at predicting a user’s interest in available services. Traditional collaborative filtering (CF) has proven to be one of the most successful techniques used in recommendation systems. However, the methods do not consider how the attribute features are related to user preferences, impacting the CF system’s prediction quality. To resolve the problem, this work proposes a novel collaborative filtering model derived from converting the user’s rating of an item to a distribution of attributes to the item. The proposed model is developed using the traditional similarity measure method. Finally, a series of experiments is performed on a typical data set, and the results indicate that the proposed model offers significant advantages in terms of improving the recommendation quality.
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.
Similar content being viewed by others
References
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for E-commerce. In: Proceedings of the Second ACM Conference on Electronic Commerce, pp. 158–167 (2000)
Mobasher, B., Burke, R.R., Bhaumik, C.: Effective attack models for shilling item-based collaborative filtering systems. In: Proc. Int. Conf. KDD Workshop on Web Mining and Web Usage Analysis, Chicago, USA, pp. 23–33 (2005)
Ja-Hwung, S., Bo-Wen, W.: Personalized rough-set-based recommendation by integrating multiple contents and collaborative information. Information Sciences 18, 113–131 (2010)
Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Proceedings of International Conference on Cooperative Information Systems, pp. 492–508 (2004)
Cho, Y.H., Kim, J.K.: Application of Web usage mining and product taxonomy to collaborative recommendations in e-commerce. Expert Systems with Applications 26, 233–246 (2004)
Leung, C.W.-K., Chan, S.C.-F., Chung, F.-L.: A collaborative filtering framework based on fuzzy association rules and multiple-level similarity. Knowledge and Information Systems 10(3), 357–381 (2006)
Shih, Y.Y., Liu, D.R.: Product recommendation approaches: Collaborative filtering via customer lifetime value and customer demands. Expert Systems with Applications 35(1–2), 350–360 (2008)
Miller, B.N., Konstan, J.A., Riedl, J.P.: Towards a personal ecommender system. ACM Transactions on Information Systems, 437–476 (2004)
Nikovski, D., Kulev(2006), V.: Induction of compact decision trees for personalized recommendation. In: Proc. Int. Conf. on the ACM Symposium on Applied Computing, Dijon, France, pp. 575–581 (2006)
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
Qian, W. (2011). A Novel Collaborative Filtering Model for Personalized Recommendation. In: Watada, J., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) Intelligent Decision Technologies. Smart Innovation, Systems and Technologies, vol 10. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22194-1_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-22194-1_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22193-4
Online ISBN: 978-3-642-22194-1
eBook Packages: EngineeringEngineering (R0)