IMPROVING ACCURACY OF MULTI-CRITERIA COLLABORATIVE FILTERING BY NORMALIZING USER RATINGS

Year 2017, Volume: 18 Issue: 1, 225 - 237, 31.03.2017

Alper Bilge , Alper Yargıç

https://doi.org/10.18038/aubtda.273802

Cited By: 3

Abstract

Multi-criteria collaborative filtering
schemes allow modeling user preferences in a more detailed manner by collecting
ratings on various aspects of a product or service. Although preferences are
expressed by numerical ratings within a predetermined scale, it is not
guaranteed that users comprehend such scale identically. As a result, profiles
of users with similar tastes might turn out to be unrelated. Besides, distinct
criteria might have different rating scales creating an essential incompatibility
with the rating schemes of users which in turn conceals proper relation between
main criterion and sub-criteria. Since users rate items based on their personal
rating habits, it is essential to determine user similarities according to
their rating patterns by normalizing ratings to an identical scale. In this
paper, two different normalization methods are studied, i.e., z-score normalization and decoupling
normalization, in order to improve accuracy of multi-criteria collaborative
filtering systems. In particular, two normalization methods are employed by
modifying the state-of-the-art memory-based multi-criteria recommender schemes
so that similarities among users are calculated based on preference models
rather than pure numerical ratings. Real world data-based experimental results
show that both methods, especially decoupling normalization method, provide
significant improvements on accuracy of estimated multi-criteria predictions
and outperform previous pure numerical ratings-based approach.

Keywords

multi-criteria collaborative filtering, z-score normalization, decoupling normalization, accuracy

References

• Ricci F, Rokach L, Shapira B. Introduction to recommender systems handbook. Springer US, 2011.
• Adomavicius G, Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE transactions on knowledge and data engineering, 2005; 17.6: 734-749.
• Manouselis N, Costopoulou C. Analysis and classification of multi-criteria recommender systems. World Wide Web, 2007; 10.4: 415-441.
• Su X, Khoshgoftaar TM. A survey of collaborative filtering techniques. Advances in artificial intelligence, 2009; 2009: 4.
• Adomavicius G, Manouselis N, Kwon Y. Multi-criteria recommender systems. In: Recommender systems handbook. Springer US, 2011. 769-803.
• Adomavicius, G, Kwon Y. New recommendation techniques for multi-criteria rating systems. IEEE Intelligent Systems, 2007; 22.3: 48-55.
• Jannach D, Karakaya Z, Gedikli F. Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM Conference on Electronic Commerce. ACM, 2012. pp. 674-689.
• Adomavicius G, Kwon Y. Multi-criteria recommender systems. In: Recommender Systems Handbook, Springer US, 2015.
• Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work. ACM, 1994. pp. 175-186.
• Jin R, Si L, Zhai C, Callan J. Collaborative filtering with decoupled models for preferences and ratings. In: Proceedings of the twelfth international conference on Information and knowledge management. ACM, 2003. pp. 309-316.
• Jin R, Si L. A study of methods for normalizing user ratings in collaborative filtering. In: Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2004. pp. 568-569.
• Nilashi M, Jannach D, bin Ibrahim, O, Ithnin N: Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 2015; 293: 235-250.
• Akhtarzada A, Calude CS, Hosking J. A multi-criteria metric algorithm for recommender systems. Fundamenta Informaticae, 2011; 110.1-4: 1-11.
• Chapphannarungsri K, Maneeroj S. Combining multiple criteria and multi-dimension for movie recommender system. In: Proceedings of the International Multi Conference of Engineers and Computer Scientists. 2009.
• Shambour Q, Lu J. Integrating multi-criteria collaborative filtering and trust filtering for personalized recommender systems. In Computational Intelligence in Multi-Criteria Decision-Making (MDCM), 2011 IEEE Symposium on IEEE, 2011. pp. 44-51.
• Lakiotaki K, Matsatsinis NF, Tsoukias A. Multi criteria user modeling in recommender systems. IEEE Intelligent Systems, 2011. 26.2: 64-76.
• Sarwar B, Karypis G, Konstan J, Riedl J. Application of dimensionality reduction in recommender system-a case study. Minnesota Univ. Minneapolis Dept. of Computer Science, 2000.
• Traupman J, Wilensky R. Collaborative quality filtering: Establishing consensus or recovering ground truth?. In: International Workshop on Knowledge Discovery on the Web. Springer Berlin Heidelberg, 2004. pp. 73-86.
• Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 1999. pp. 230-237.
• Naak A, Hage H, Aimeur E. A multi-criteria collaborative filtering approach for research paper recommendation in papyres. In: International Conference on E-Technologies. Springer Berlin Heidelberg, 2009. pp. 25-39.
• Manouselis N, Kyrgiazos G, Stoitsis G, Stoitsis J. Revisiting the multi-criteria recommender system of a learning portal. In: Proceedings of the 2nd Workshop on Recommender Systems in Technology Enhanced Learning. 2012. pp. 35-48.
• Manouselis N, Vuorikari R, Van Assche F. Simulated analysis of MAUT collaborative filtering for learning object recommendation. In: Proceedings of the 1st Workshop on Social Information Retrieval for Technology Enhanced Learning. 2007. pp. 27-35.
• Han J, Pei J, Kamber M. Data mining: concepts and techniques. Elsevier, 2011.