CONSTRUCTION OF PATTERNS OF USER PREFERENCES DYNAMICS FOR EXPLANATIONS IN THE RECOMMENDER SYSTEM

Main Article Content

Serhii Chalyi
Volodymyr Leshchynskyi

Abstract

The subject of study in the article is the processes of constructing explanations in recommendation systems. Objectives. The goal is to develop a method of constructing patterns that reflect the dynamics of user preferences and provide an opportunity to form an explanation of the recommended list of items, taking into account changes in the user’s requirements of the recommendation system. Construction of explanations taking into account the dynamics of changes in consumer preferences makes it possible to increase user confidence in the results of the intelligent system. Tasks: structuring models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system; development of a method for constructing patterns of changing user preferences using process mining technology; experimental verification of the method for constructing patterns of changing consumer preferences. The approaches used are: temporal logics, which determine the approaches to the description of the temporal ordering of a set of events. The following results are obtained. The structuring of models of temporal patterns of parallel-alternative and sequential-alternative users’ choice of the recommendation system is performed; developed and performed an experimental test of the method of constructing patterns of user preferences dynamics. Conclusions. The scientific novelty of the results is as follows. The method of dynamics patterns construction of users’ preferences for the formation of explanations concerning the recommended list of subjects is offered. The method sequentially generates a set of ordered events, each of which reflects the choice of the subject by a group of users at a certain time interval, and also builds a graph representation of the patterns of user preferences through intellectual analysis of processes. The patterns obtained as a result of the method consist of time-ordered pairs of events that reflect the knowledge of changing user preferences over time. Further use of such dependencies as elements of the knowledge base makes it possible based on probabilistic inference to build a set of alternative explanations for the received recommendation, and then arrange these explanations according to the probability of their implementation for the recommended list of subjects.

Article Details

Section
Intelligent information systems
Author Biographies

Serhii Chalyi, Kharkiv National University of RadioElectronics, Kharkiv

Doctor of Technical Sciences, Professor, Professor of Information Control Systems Department

Volodymyr Leshchynskyi, Kharkiv National University of RadioElectronics, Kharkiv

Candidate of Technical Sciences, Associate Professor, Associate Professor of Software Engineering Department

References

Ricci, F., Rockach, L., Shapira, B. and Kantor, P.B. (2011), Recommender Systems Handbook, Springer US, 842 p., DOI: https://doi.org/10.1007/978-0-387-85820-3

Cami, B.R., Hassanpour, H. and Mashayekhi, H. (2017), “A content-based movie recommender system based on temporal user preferences”, 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), Shahrood, Iran, pp. 121-125. 920-929, DOI: https://doi.org/10.1109/ICSPIS.2017.8311601.

Hernández-Rubio, M., Cantador, I. and Bellogín, A. (2019), “A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews”, User Model User-Adap Inter, 29, pp. 381–441, DOI: https://doi.org/10.1007/s11257-018-9214-9.

Chala, O., Novikov, L. and Chernyshova, L. (2019), “Method for detecting shilling attacks in e-commerce systems using weighted temporal rules”, EUREKA: Physics and Engineering, vol. 5, pp. 29-36. DOI: https://doi.org/10.21303/2461-4262.2019.00983.

Chala, O., Novikov, L. and Chernyshova, L. (2020), “Method for detecting shilling attacks in recommendater systems using objective feedback”, EUREKA: Physics and Engineering, vol. 5, pp. 21-30, DOI: https://doi.org/10.21303/2461-4262.2020.001394.

Chalyi, S., Leshchynskyi, V. and Leshchynska I. (2019), “The concept of designing explanations in the recommender systems based on the white box”, Control, navigation and communication systems, Vol. 3 (55). pp. 156-160, DOI: https://doi.org/10.26906/SUNZ.2019.3.156.

Adomavicius, G. and Tuzhilin, A. (2005), “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, Knowledge and Data Engineering, IEEE Transactions on, vol. 17, is. 6, pp. 734-749, DOI: https://doi.org/10.1109/TKDE.2005.99.

Zachary C.L. (2016), “The mythos of model interpretability”, Communications of the ACM, arXiv:1606.03490, pp. 1-6.

Ribeiro, M.T., Singh, S. and Guestrin, C. (2016), “Why should I trust you?: Explaining the predictions of any classifier”, Proc. of the 22nd ACM SIGKDD International Conference on Knowledge discovery and data mining, pp. 1135–1144. DOI: https://doi.org/10.1145/2939672.2939778.

Miller, T. (2019), “Explanation in artificial intelligence: Insights from the social sciences”, Artificial Intelligence, vol. 267, pp.1-38, DOI: https://doi.org/10.1016/j.artint.2018.07.007.

Mueller, K. (2019), “Advances in Visualization Recommender Systems”, Computer, vol. 52, no. 8, pp. 4-5, DOI: https://doi.org/10.1109/MC.2019.2918513.

Nan, D., Yichen, W., Niao, H. and Le S. (2015), “Time-Sensitive Recommendation From Recurrent User Activities”, Proceedings of the 28th International Conference on Neural Information Processing Systems (NIPS'15), Vol. 2, pp. 3492–3500.

Sun, F., Zhuang, H., Zhang, J., Wang, Z. and Zheng K. (2020), “Personalized recommendation algorithm considering time sensitivity”, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer, vol. 322, pp 154-162, DOI: https://doi.org/10.1007/978-3-030-48513-9_12.

Kille, B., Lommatzsch, A. and Brodt T. (2015), “News Recommendation in Real-Time”, Smart Information Systems. Advances in Computer Vision and Pattern Recognition, Springer, Cham, pp. 149-180, DOI: https://doi.org/10.1007/978-3-319-14178-7_6.

Chalyi, S., Leshchynskyi, V. and Leshchynska, I. (2019), “Method of forming recommendations using temporal constraints in a situation of cyclic cold start of the recommender system”, EUREKA: Physics and Engineering, 4, pp. 34-40, DOI: https://doi.org/10.21303/2461-4262.2019.00952.

Rabiu, I., Naomie, S., Aminu, D. and Akram, O. (2020), “Recommender system based on temporal models: A Systematic Review”, Applied Sciences, Vol. 10(7), p. 2204, DOI: https://doi.org/10.3390/app10072204.

Wang, C., Min, Z., Ma, W. and Liu, Y. (2019), “Modeling item-specific temporal dynamics of repeat consumption for recommender systems”, WWW '19- The World Wide Web Conference, pp. 1977–1987, DOI:

https://doi.org/10.1145/3308558.3313594.

Chalyi, S. and Pribylnova, I. (2019), “The method of constructing recommendations online on the temporal dynamics of user interests using multilayer graph”, EUREKA: Physics and Engineering, vol. 3, pp. 13-19, DOI: https://doi.org/10.21303/2461-4262.2019.00894.

Levykin, V. and Chala, O. (2018), “Development of a method for the probabilistic inference of sequences of a business process activities to support the business process management”, Eastern-European Journal of Enterprise Technologies, vol. 3, iss. 95, pp. 16–24.

Chalyi, S., Leshchynskyi, V. and Leshchynska, I. (2019), “Modeling explanations for the recommended list of items based on the temporal dimension of user choice”, Control, navigation and communication systems, Vol. 6 (58), pp. 97-101. DOI: https://doi.org/10.26906/SUNZ.2019.6.097.

Chu, W. and Park, S.-T. (2009), “Personalized recommendation on dynamic content using predictive bilinear models”, International Conference on World Wide Web, pp. 691-700, DOI: https://doi.org/10.1145/1526709.1526802.

Levykin, V. and Chala, O. (2018), “Method of determining weights of temporal rules in Markov logic network for building knowledge base in information control systems”, EUREKA: Physics and Engineering, vol. 5, pp. 3-10, DOI: http://dx.doi.org/10.21303/2461-4262.2018.00713.

Chalyi, S. and Leshchynskyi, V. (2020), “Method of constructing explanations for recommender systems based on the temporal dynamics of user preferences”, EUREKA: Physics and Engineering, vol. 3, pp. 43-50, DOI: https://doi.org/10.21303/2461-4262.2020.001228.

Chalyi, S., Leshchynskyi, V. and Leshchynska, I. (2020), “Detailing explanations in the recommender system based on matching temporal knowledge”, Eastern-European Journal of Enterprise Technologies, vol 4, no 2, pp. 6-13, DOI: https://doi.org/10.15587/1729-4061.2020.210013.