Abstract
The traditional similarity algorithm in collaborative filtering mainly pay attention to the similarity or correlation of users’ ratings, lacking the consideration of difference of users’ ratings. In this paper, we divide the relationship of users’ ratings into differential part and correlated part, proposing a similarity measurement based on the difference and the correlation of users’ ratings which performs well with non-sparse dataset. In order to solve the problem that the algorithm is not accurate in spare dataset, we improve it by prefilling the vacancy of rating matrix. Experiment results show that this algorithm improves significantly the accuracy of the recommendation after prefilling the rating matrix.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Eppler, M.J., Mengis, J.: The concept of information overload: a review of literature from organization science, accounting, marketing, MIS, and related disciplines. Inf. Soc. 38, 325–344 (2004)
Sivapalan, S., Sadeghian, A., Rahnama, H., Madni, A.M.: Recommender systems in e-commerce. In: World Automation Congress, pp. 179–184 (2014)
Renda, M.E., Straccia, U.: A personalized collaborative digital library environment: a model and an application. Inf. Process. Manag. 41, 5–21 (2005)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40(3), 77–87 (2000). Article (Konstan2000GroupLens)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: International Conference on World Wide Web, pp. 285–295 (2001)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2013)
Cacheda, F., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web 5, 1–33 (2011)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering, pp. 43–52 (1998)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)
Singh, A., Yadav, A., Rana, A.: K-means with three different distance metrics. Int. J. Comput. Appl. 67, 13–17 (2013)
Shardanand, U.: Social information filtering for music recommendation. Massachusetts Institute of Technology, pp. 74–81 (1994)
Liu, Y., Feng, J., Lu, J.: Collaborative filtering algorithm based on rating distance. In: International Conference on Ubiquitous Information Management and Communication, p. 66 (2017)
Jang, S., Yang, J., Kim, D.K.: Minimum MSE design for multiuser MIMO relay. IEEE Commun. Lett. 14, 812–814 (2010)
Eldar, Y.C.: Universal weighted MSE improvement of the least-squares estimator. IEEE Trans. Sig. Process. 56, 1788–1800 (2008)
Han, Y., Cao, H., Liu, L.: Collaborative filtering recommendation algorithm based on score matrix filling and user interest. Comput. Eng. (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Cai, Zh., Wang, Js., Li, Yk., Liu, Sb. (2017). A Collaborative Filtering Recommendation Algorithm Based on the Difference and the Correlation of Users’ Ratings. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-6385-5_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6384-8
Online ISBN: 978-981-10-6385-5
eBook Packages: Computer ScienceComputer Science (R0)