All Title Author
Keywords Abstract


Proposing a New Metric for Collaborative Filtering

DOI: 10.4236/jsea.2011.47047, PP. 411-416

Keywords: Recommendation Systems, Collaborative filtering, Similarity Metric

Full-Text   Cite this paper   Add to My Lib

Abstract:

The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.

References

[1]  X. Su and T. Khoshgoftaar, “Imputed Neighborhood Based Collaborative Filtering,” Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, 9-12 December 2008, pp. 633-639.
[2]  M. Deshpande and G. Karypis, “Item-Based Top-N Recommendation Algorithms,” ACM Transactions on Information Systems, Vol. 22, No. 1, 2004, pp. 143-177. doi:10.1145/963770.963776
[3]  G. Karypis, “Evaluation of Item-Based Top-N Recommendation Algorithms,” Proceedings of the 10th International Conference on Information and Knowledge Management, Atlanta, 5-10 November 2001, pp. 247-254.
[4]  L. He and F. Wu, “A Time-Context-Based Collaborative Filtering Algorithm,” IEEE International Conference on Granular Computing, Nanchang, 17-19 August 2009, pp. 209-213.
[5]  N. Lathia, “Evaluating Collaborative Filtering over Time,” Ph.D. Thesis, University College London, London, 2010.
[6]  M. Charikar, “Similarity Estimation Techniques from Rounding Algorithms,” Annual ACM Symposium on The- ory of Computing, Montreal, 19-21 May 2002, pp. 380-388.
[7]  P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, “Grouplens: An Open Architecture for Collaborative Filtering of Netnews,” ACM Conference on Computer Supported Cooperative Work, New York, 22-26 October 1994, pp. 175-186.
[8]  B. M. Sarwar, G. Karypis, J. A. Konstan and J. Riedl, “Analysis of Recommendation Algorithms for E-Commerce,” ACM Conference on Electronic Commerce, New York, 17-20 October 2000, pp. 158-167.
[9]  G. Salton and M. McGill, “Introduction to Modern Information Retrieval,” Facet Publishing, New York, 1983.
[10]  S. Ma, X. Li, Y. Ding and M. E. Orlowska, “A Recommender System with Interest-Drifting,” 8th International Conference on Web Information Systems Engineering, Nancy, 3-7 December 2007, pp. 633-642.
[11]  T. Segaran, “Programming Collective Intelligence,” O'R-eilly Media, Sebastopol, 2007.
[12]  K. Goldberg, T. Roeder, D. Gupta and C. Perkins, “Eigentaste: A Constant Time Collaborative Filtering Algorithm,” Information Retrieval, Vol. 4, No. 2, 2001, pp. 133-151. doi:10.1023/A:1011419012209
[13]  J. L. Herlocker and J. A. Konstan, “Evaluating Collaborative Filtering Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, 2004, pp. 5-53. doi:10.1145/963770.963772
[14]  X. Su and T. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Advances in Artificial Intelligence, Vol. 2009, 2009, pp. 1-20. doi:10.1155/2009/421425

Full-Text

comments powered by Disqus