Abstract
Collaborative filtering recommendation system based on user similarity has been wildly studied because of its broad application. In reality, users keep partial similarity with larger possibility. Computing the whole similarity between users without considering item category is inaccurate when predicting rating for a special category of items by using collaborative filtering recommendation system. Aiming at this problem, a new similarity measurement was given. Based on the new similarity measurement, a new collaborative filtering algorithm named UICF was presented for recommendation. When predicting rating for the special item, UICF chooses the users as nearest neighbors which have the similar rating feature for the items with the same type of the special item, instead of for all the items. Experimental results show the higher quality of the algorithm.
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
Chickering, D., Hecherman, D.: Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning 29(2/3), 181–212 (1997)
Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39, 1–38 (1997)
Thiesson B., Meek C., Chickering D., Heckerman D.: Learning mixture of DAG models. Technical Report, MSR-TR-97-30, Redmond: Microsoft Research (1997)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for E-commerce. In: ACM Conference on Electronic Commerce, pp. 158–167 (2000)
Wolf, J., Aggarwal, C., Wu, K.L., Yu, P.: Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In: Proceedings of the ACM SIGMOD International Conference on Knowledge Discovery and Data Mining, pp. 201–212 (1999)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Pro. of the 10th Conf. on Information and Knowledge Management, pp. 247–254 (2001)
Sarwar, B., Karypis, G., Konstan, J.: Item-based collaborative filtering recommendation algorithms. In: Proc. of the 10th Int Conf. on Word Wide Web, pp. 285–295 (2001)
Chunxiao, X., Fengrong, G., Sinan, Z., et al.: A collaborative filtering recommendation algorithm incorporated with user interest change. Journal of computer Research and Development 44(2), 296–301 (2007) (in Chinese)
Liang, Z., Naijing, H., Shouzhi, Z.: Algorithm design for personalization recommendation systems. Journal of Computer Research and Development 39(8), 986–991 (2002) (in Chinese)
Junfeng, Z., Xian, T., Jingfeng, G.: An optimized collaborative filtering recommendation algorithm. Journal of Computer Research and Development 41(10), 1842–1847 (2004) (in Chinese)
Guangwei, Z., Deyi, L., Peng, L., Jianchu, K., Guisheng, C.: A Collaborative Filtering Recommendation Algorithm Based on Cloud Model. Journal of Software 18, 2403–2411 (2007) (in Chinese)
Shuliang, W., Yuan, X., Meng, F.: A Collaborative Filtering Recommendation Algorithm Based on Item and Cloud Model. Wuhan University Journal of Natural Sciences 16, 016–020 (2011) (in Chinese)
Zhang, J., Pu, P.: A recursive prediction algorithm for collaborative filtering recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 57–64 (2007)
Dy, L.: Artificial Intelligence with Uncertainty. National Defense Industry Press, Beijing (2005) (in Chinese)
Dy, L., Cy, L.: Study on the universality of the normal cloud model. Engineering Science 6(8), 28–34 (2004) (in Chinese)
Dy, L., Cy, L., Du, Y., Han, X.: Artificial intelligence with uncertainty. Journal of Software 15(11), 1583–1594 (2004) (in Chinese)
Dy, L.: Uncertainty in knowledge representation. Engineering Science 2(10), 73–79 (2000) (in Chinese)
BinQuan, Z.: A collaborative filtering recommendation algorithm based on domain knowledge. Computer Engineering 31(21), 7–9 (2005) (in Chinese)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-Based collaborative filtering recommendation algorithms. In: Proc. of the 10th World Wide Web Conf., pp. 285–295 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, D., Xu, C. (2012). A Collaborative Filtering Recommendation System by Unifying User Similarity and Item Similarity. In: Wang, L., Jiang, J., Lu, J., Hong, L., Liu, B. (eds) Web-Age Information Management. WAIM 2011. Lecture Notes in Computer Science, vol 7142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28635-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-28635-3_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28634-6
Online ISBN: 978-3-642-28635-3
eBook Packages: Computer ScienceComputer Science (R0)