ISSN 2071-8594

Russian academy of sciences


Gennady Osipov

A .V. Smirnov, A. V. Ponomarev Multi-Criteria Context-Driven Recommender Systems: Model and Method


A model and method of generating context-driven recommendations for recommendation systems with multi-criteria ratings are proposed, applicable when the user's attitude to the object is fixed not by using one integral criterion (assessment, overall rating), but by using a set of individual criteria that evaluate different aspects of the object. The proposed model and method allow to solve two main problems of using recommender systems: to rank objects according to the predicted subjective integral utility with given weights of partial criteria and to rank objects according to the predicted subjective integral utility in a given context.


recommendation systems, recommender systems, multi-criteria optimization, weighted sum method, collaborative filtering, content filtering, context-driven systems.

PP. 24-31.

DOI 10.14357/20718594190303


1. Sivapalan, S. et al. 2014. Recommender systems in ecommerce. World Automation Congress (WAC). 179–184.
2. Mbugua, A.W., and A.O. Omondi. 2017. An Application of association rule learning in recommender systems for e-Commerce and its effect on marketing. IEEE Pan African Conference on Science, Computing, and Telecommunications.
3. Paraschakis, D., B.J. Nilsson, and J. Holländer. 2015. Comparative evaluation of top-n recommenders in ecommerce: An industrial perspective. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). 1024–1031.
4. Zhou, M. et al. 2018. Micro behaviors: A new perspective in e-commerce recommender systems. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 727–735.
5. Gavalas, D. et al. 2014. Mobile recommender systems in tourism. Journal of network and computer applications. 39:319–333.
6. Borràs, J., A. Moreno, and A. Valls. 2014. Intelligent tourism recommender systems: A survey. Expert Systems with Applications. 41(16):7370–7389.
7. Kashevnik, A., A. Ponomarev, and A. Smirnov. 2017. A Multimodel Context-Aware Tourism Recommendation Service: Approach and Architecture. Journal of Computer and System Sciences International. 56(2):245–258.
8. Karimi, M., D. Jannach, and M. Jugovac. 2018. News recommender systems – Survey and roads ahead. Information Processing & Management. 54(6):1203–1227.
9. Amato, F., V. Moscato, A. Picariello, and F. Piccialli. 2019. SOS: A multimedia recommender system for online social networks. Future Generation Computer Systems. 93:914–923.
10. Teran, L., A.O. Mensah, and A. Estorelli. 2018. A literature review for recommender systems techniques used in microblogs. Expert Systems with Applications. 103:63–73.
11. Adomavicius, G., R. Sankaranarayanan, S. Sen, and A. Tuzhilin. 2005. Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inf. Syst. 23(1):103–145.
12. Villegas, N.M. et al. 2018. Characterizing context-aware recommender systems: A systematic literature review. Knowledge-Based Systems. 140:173–200.
13. Haruna, K. et al. 2017. Context-Aware Recommender System: A Review of Recent Developmental Process and Future Research Direction. Applied Sciences. 7(12):1211.
14. Ribeiro, M., A. Lacerda, E. de Moura, A. Veloso, and N. Ziviani. 2013. Multi-objective pareto-efficient approaches for recommender systems. ACM Transactions on Intelligent Systems and Technology. 9(1):1–20.
15. Adomavicius, G., Y. Kwon. 2007. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems. 22(3):48–55.
16. Pazzani, M., and D. Billsus. 2007. Content-based recommendation systems. In The Adaptive Web. 325–341.
17. Ricci, F. et al. 2015. Recommender Systems Handbook. 2nd edition. Springer. 1003 p.
18. Koren, Y. 2009. Matrix Factorization Techniques for Recommender Systems. Computer. 42(8):30–37.
19. Gareth, J., D. Witten, T. Hastie, and R. Tibshirani. 2013. An Introduction to Statistical Learning: with Applications in R. Springer.