Effects of Binary Vectors Similarities on the Accuracy of Multi-Criteria Collaborative Filtering

Abstract

Recommender systems offer tailored recommendations by employing various algorithms, and collaborative filtering is one of the well-known and commonly used of those. A traditional collaborative filtering system allows users to rate on a single criterion. However, a single criterion may be insufficient to indicate preferences in domains such as restaurants, movies, or tourism. Multi-criteria collaborative filtering provides a multi-dimensional rating option. In similarity-based multi-criteria collaborative filtering schemes, existing similarity methods utilize co-users or co-items regardless of how many there are. However, a high correlation with a few co-ratings does not always provide a reliable neighborhood. Therefore, it is very common, in both single- and multi-criteria collaborative filtering, to weight similarities with functions utilizing the number of co-ratings. Since multi-criteria collaborative filtering is yet growing, it lacks a comprehensive view of the effects of similarity weighting. This work studies multi-criteria collaborative filtering and the literature of binary vector similarities, which are frequently used for weighting, by giving a related taxonomy and conducts extensive experiments to analyze the effects of weighting similarities on item- and user-based multi-criteria collaborative filtering. Experimental findings suggest that prediction accuracy of item-based multi-criteria collaborative filtering can be boosted by especially binary vector similarity measures which do not consider mutual absences.

Keywords

• multi-criteria
• collaborative filtering
• similarity-weighting
• binary vector similarity

References

1. D. Goldberg, D. Nichols, B. M. Oki, and D. Terry, “Using collaborative filtering to weave an information tapestry,” Commun. ACM, vol. 35, no. 12, pp. 61–70, Dec. 1992, doi: 10.1145/138859.138867.
2. M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, “Evaluating Collaborative Filtering Recommender Algorithms: A Survey,” IEEE Access, vol. 6, pp. 74003–74024, 2018, doi: 10.1109/ACCESS.2018.2883742.
3. S. Chen and Y. Peng, “Matrix factorization for recommendation with explicit and implicit feedback,” Knowledge-Based Systems, vol. 158, pp. 109–117, Oct. 2018, doi: 10.1016/j.knosys.2018.05.040.
4. J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, vol. 26, pp. 225–238, Feb. 2012, doi: 10.1016/j.knosys.2011.07.021.
5. C. C. Aggarwal, Recommender Systems: The Textbook, 1st ed. 2016. Cham: Springer International Publishing: Imprint: Springer, 2016.
6. G. Adomavicius and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intell. Syst., vol. 22, no. 3, pp. 48–55, May 2007, doi: 10.1109/MIS.2007.58.
7. J. Herlocker, J. A. Konstan, and J. Riedl, “An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms,” Information Retrieval, vol. 5, no. 4, pp. 287–310, Oct. 2002, doi: 10.1023/A:1020443909834.
8. H. Ma, I. King, and M. R. Lyu, “Effective missing data prediction for collaborative filtering,” in Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR ’07, Amsterdam, The Netherlands, 2007, p. 39. doi: 10.1145/1277741.1277751.

9. N. Polatidis and C. K. Georgiadis, “A multi-level collaborative filtering method that improves recommendations,” Expert Systems with Applications, vol. 48, pp. 100–110, Apr. 2016, doi: 10.1016/j.eswa.2015.11.023.
10. N. Polatidis and C. K. Georgiadis, “A dynamic multi-level collaborative filtering method for improved recommendations,” Computer Standards & Interfaces, vol. 51, pp. 14–21, Mar. 2017, doi: 10.1016/j.csi.2016.10.014.
11. L. Candillier, F. Meyer, and F. Fessant, “Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems,” in Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, 2008, pp. 242–255.
12. E. Sadikoğlu and B. D. Okkalioğlu, “Increasing Prediction Performance Using Weighting Methods in Multi-Criteria Item-Based Collaborative Filtering,” European Journal of Science and Technology, pp. 110–121, Aug. 2020, doi: 10.31590/ejosat.779171.
13. E. Yalçin and A. Bi̇lge, “Binary multicriteria collaborative filtering,” Turk J Elec Eng & Comp Sci, vol. 28, no. 6, pp. 3419–3437, Nov. 2020.
14. M. Plantié, J. Montmain, and G. Dray, “Movies Recommenders Systems: Automation of the Information and Evaluation Phases in a Multi-criteria Decision-Making Process,” in Database and Expert Systems Applications, 2005, pp. 633–644.
15. A. Bilge and C. Kaleli, “A multi-criteria item-based collaborative filtering framework,” in 2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), Chon Buri, May 2014, pp. 18–22. doi: 10.1109/JCSSE.2014.6841835.
16. Q. Shambour and J. Lu, "A Hybrid Multi-criteria Semantic-Enhanced Collaborative Filtering Approach for Personalized Recommendations," 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 71-78, doi: 10.1109/WI-IAT.2011.109.
17. Q. Shambour, M. Hourani, and S. Fraihat, “An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, 2016, doi: 10.14569/IJACSA.2016.070837.
18. Q. Shambour, “A user-based multi-criteria recommendation approach for personalized recommendations,” International Journal of Computer Science and Information Security, vol. 14, no. 12, p. 657, 2016.
19. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” in Proceedings of the 1994 ACM conference on Computer supported cooperative work - CSCW ’94, Chapel Hill, North Carolina, United States, 1994, pp. 175–186. doi: 10.1145/192844.192905.
20. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the tenth international conference on World Wide Web - WWW ’01, Hong Kong, Hong Kong, 2001, pp. 285–295. doi: 10.1145/371920.372071.
21. J. Wei, J. He, K. Chen, Y. Zhou, and Z. Tang, “Collaborative filtering and deep learning based recommendation system for cold start items,” Expert Systems with Applications, vol. 69, pp. 29–39, Mar. 2017, doi: 10.1016/j.eswa.2016.09.040.
22. H.-J. Kwon, T.-H. Lee, J.-H. Kim, and K.-S. Hong, “Improving Prediction Accuracy Using Entropy Weighting in Collaborative Filtering,” in 2009 Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, Jul. 2009, pp. 40–45. doi: 10.1109/UIC-ATC.2009.50.
23. M. Ghazanfar and A. Prugel-Bennett, “Novel Significance Weighting Schemes for Collaborative Filtering: Generating Improved Recommendations in Sparse Environments,” presented at the DMIN’10, the 2010 International Conference on Data Mining!, Jul. 12, 2010. Accessed: Jun. 02, 2021. [Online]. Available: https://eprints.soton.ac.uk/270846/
24. J. Han and M. Kamber, Data mining. Burlington, MA: Elsevier, 2012.
25. S. Choi and S. Cha, “A survey of Binary similarity and distance measures,” Journal of Systemics, Cybernetics and Informatics, pp. 43–48, 2010.
26. J. Podani, “Distance, similarity, correlation…”, in Introduction to the exploration of multivariate biological data. Leiden: Backhuys Publishers, 2000.
27. S.-H. Cha, C. Tappert, and S. Yoon, “Enhancing binary feature vector similarity measures,” J. Pattern Recognit. Res., vol. 1, no. 1, pp. 63–77, 2006. doi: 10.13176/11.20.
28. D. Jannach, Z. Karakaya, and F. Gedikli, “Accuracy improvements for multi-criteria recommender systems,” in Proceedings of the 13th ACM Conference on Electronic Commerce - EC ’12, Valencia, Spain, 2012, p. 674. doi: 10.1145/2229012.2229065.