High Order Profile Expansion to tackle the new user problem on recommender systems


Autoři: Diego Fernández aff001;  Vreixo Formoso aff001;  Fidel Cacheda aff001;  Victor Carneiro aff001
Působiště autorů: Center for Information and Communications Technology Research (CITIC), Department of Computer Science and Information Technologies, University of A Coruña, A Coruña, Spain aff001
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224555

Souhrn

Collaborative Filtering algorithms provide users with recommendations based on their opinions, that is, on the ratings given by the user for some items. They are the most popular and widely implemented algorithms in Recommender Systems, especially in e-commerce, considering their good results. However, when the information is extremely sparse, independently of the domain nature, they do not present such good results. In particular, it is difficult to offer recommendations which are accurate enough to a user who has just arrived to a system or who has rated few items. This is the well-known new user problem, a type of cold-start. Profile Expansion techniques had been already presented as a method to alleviate this situation. These techniques increase the size of the user profile, by obtaining information about user tastes in distinct ways. Therefore, recommender algorithms have more information at their disposal, and results improve. In this paper, we present the High Order Profile Expansion techniques, which combine in different ways the Profile Expansion methods. The results show 110% improvement in precision over the algorithm without Profile Expansion, and 10% improvement over Profile Expansion techniques.

Klíčová slova:

Algorithms – Decision making – Decision theory – Decision tree learning – Experimental design – Human learning – Social networks – Similarity measures


Zdroje

1. Goldberg D, Nichols D, Oki BM, Terry D. Using collaborative filtering to weave an information Tapestry. Communications of the ACM. 1992;35(12):61–70. doi: 10.1145/138859.138867

2. Herlocker JL, Konstan JA, Terveen LG, Riedl JT. Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst. 2004;22(1):5–53. http://doi.acm.org/10.1145/963770.963772.

3. Sarumathi M, Singarani S, Thameemaa S, Umayal V, Archana S, Indira K, et al. Systematic approach for cold start issues in recommendations system. In: 2016 International Conference on Recent Trends in Information Technology (ICRTIT). IEEE; 2016. p. 1–7.

4. Schein AI, Popescul A, Ungar LH, Pennock DM. Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. SIGIR’02. New York, NY, USA: ACM; 2002. p. 253–260.

5. Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA, et al. Getting to Know You: Learning New User Preferences in Recommender Systems. In: Proceedings of the 7th International Conference on Intelligent User Interfaces. IUI’02. New York, NY, USA: ACM; 2002. p. 127–134. Available from: http://doi.acm.org/10.1145/502716.502737.

6. Formoso V, Fernández D, Cacheda F, Carneiro V. Using profile expansion techniques to alleviate the new user problem. Inf Process Manage. 2013;49(3):659–672. doi: 10.1016/j.ipm.2012.07.005

7. Herlocker J, Konstan JA, Riedl J. An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms. Inf Retr. 2002;5(4):287–310. http://dx.doi.org/10.1023/A:1020443909834.

8. Desrosiers C, Karypis G. A Comprehensive Survey of Neighborhood-based Recommendation Methods. In: Ricci F, Rokach L, Shapira B, Kantor PB, editors. Recommender Systems Handbook. Springer; 2011. p. 107–144.

9. Zhang C, Liu J, Qu Y, Han T, Ge X, Zeng A. Enhancing the robustness of recommender systems against spammers. PloS one. 2018;13(11):e0206458. https://doi.org/10.1371/journal.pone.0206458. 30383766

10. 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. CSCW’94. New York, NY, USA: ACM; 1994. p. 175–186.

11. Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. UAI’98. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.; 1998. p. 43–52. Available from: http://dl.acm.org/citation.cfm?id=2074094.2074100.

12. Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web. WWW’01. New York, NY, USA: ACM; 2001. p. 285–295. Available from: http://doi.acm.org/10.1145/371920.372071.

13. Rashid AM, Karypis G, Riedl J. Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter. 2008;10:90–100. http://doi.acm.org/10.1145/1540276.1540302.

14. Elahi M, Repsys V, Ricci F. Rating Elicitation Strategies for Collaborative Filtering E-Commerce and Web Technologies. vol. 85 of Lecture Notes in Business Information Processing. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011. p. 160–171. Available from: http://dx.doi.org/10.1007/978-3-642-23014-1_14.

15. Cremonesi P, Garzottto F, Turrin R. User effort vs. accuracy in rating-based elicitation. In: Proceedings of the sixth ACM conference on Recommender systems. RecSys’12. New York, NY, USA: ACM; 2012. p. 27–34. Available from: http://doi.acm.org/10.1145/2365952.2365963.

16. Golbandi N, Koren Y, Lempel R. Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the fourth ACM international conference on Web search and data mining. WSDM’11. New York, NY, USA: ACM; 2011. p. 595–604. Available from: http://doi.acm.org/10.1145/1935826.1935910.

17. Rokach L, Kisilevich S. Initial Profile Generation in Recommender Systems Using Pairwise Comparison. IEEE Transactions on Systems, Man, and Cybernetics, Part C. 2012;42(6):1854–1859. doi: 10.1109/TSMCC.2012.2197679

18. Feil S, Kretzer M, Werder K, Maedche A. Using gamification to tackle the cold-start problem in recommender systems. In: Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion. ACM; 2016. p. 253–256.

19. Silva N, Carvalho D, Pereira ACM, Mourão F, Rocha L. The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems. 2019;80:1–12. https://doi.org/10.1016/j.is.2018.09.001.

20. Cacheda F, Carneiro V, Fernández D, Formoso V. Improving k-nearest neighbors algorithms: practical application of dataset analysis. In: Proceedings of the 20th ACM international conference on Information and knowledge management. CIKM’11. New York, NY, USA: ACM; 2011. p. 2253–2256. Available from: http://doi.acm.org/10.1145/2063576.2063939.

21. Ahn HJ. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci. 2008;178:37–51. doi: 10.1016/j.ins.2007.07.024

22. Bobadilla J, Ortega F, Hernando A, Bernal J. A collaborative filtering approach to mitigate the new user cold start problem. Know-Based Syst. 2012;26:225–238. doi: 10.1016/j.knosys.2011.07.021

23. Piccart B, Struyf J, Blockeel H. Alleviating the Sparsity Problem in Collaborative Filtering by Using an Adapted Distance and a Graph-Based Method. In: SIAM International Conference on Data Mining. SDM 2010; 2010. p. 189–198. Available from: http://www.odysci.com/article/1010112988297985.

24. Baeza-Yates R, Ribeiro-Neto B. Modern Information Retrieval: The Concepts and Technology Behind Search. 2nd ed. USA: Addison-Wesley Publishing Company; 2011.

25. Cacheda F, Carneiro V, Fernández D, Formoso V. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web. 2011;5:2:1–2:33. http://doi.acm.org/10.1145/1921591.1921593.

26. Bennett J, Lanning S. The Netflix Prize. In: Proceedings of KDD Cup and Workshop. KDDCup’07. San Jose, California, USA: ACM; 2007. p. 3–6.


Článek vyšel v časopise

PLOS One


2019 Číslo 11