Serhii Chalyi, Inna Pribylnova


The problem of the online construction of a rating list of objects in the recommender system is considered. A method for constructing recommendations online using the presentation of input data in the form of a multi-layer graph based on changes in user interests over time is proposed. The method is used for constructing recommendations in a situation with implicit feedback from the user. Input data are represented by a sequence of user choice records with a time stamp for each choice. The method includes the phases of pre-filtering of data and building recommendations by collaborative filtering of selected data. At pre-filtering of the input data, the subset of data is split into a sequence of fixed-length non-overlapping time intervals. Users with similar interests and records with objects of interest to these users are selected on a finite continuous subset of time intervals. In the second phase, the pre-filtered subset of data is used, which allows reducing the computational costs of generating recommendations. The method allows increasing the efficiency of building a rating list offered to the target user by taking into account changes in the interests of the user over time.


recommender system; collaborative filtering; multi-layer graph; online recommendations; content personalization; area under the error curve

Full Text:



Ricci, F., Rokach, L., Shapira, B. (Eds.) (2015). Recommender Systems. Springer, 1008. doi:

Amatriain, X. (2013). Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter, 14 (2), 37–48. doi:

Aggarwal, C. (2017). Recommender Systems. Springer, 498. doi:

Amatriain, X. (2013). Big & personal. Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining Algorithms, Systems, Programming Models and Applications – BigMine ’13. doi:

Jones, M. T. (2013). Recommender systems, Part 1. Introduction to approaches and algorithms. Available at:

Schafer, J. B., Konstan, J., Riedi, J. (1999). Recommender systems in e-commerce. Proceedings of the 1st ACM Conference on Electronic Commerce – EC ’99. doi:

Koren, Y. (2009). Collaborative filtering with temporal dynamics. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD ’09. doi:

Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J. (2010). Temporal recommendation on graphs via long- and short-term preference fusion. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD ’10. doi:

Luo, C., Cai, X., Chowdhury, N. (2014). Self-training Temporal Dynamic Collaborative Filtering. Lecture Notes in Computer Science, 461–472. doi:

Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y., Chua, T., Jin, D. (2018). Learning Recommender Systems from Multi-Behavior Data. IEEE Transactions on knowledge and data engineering, 1–12.

Bakir, C. (2018). Collaborative Filtering with Temporal Dynamics with Using Singular Value Decomposition. Technical gazette, 25 (1), 130–135. doi:

Chalyi, S., Levykin, I., Petrychenko, A., Bogatov, I. (2018). Causality-based model checking in business process management tasks. 2018 IEEE 9th International Conference on Dependable Systems, Services and Technologies (DESSERT). doi:

Kalynychenko, O., Chalyi, S., Bodyanskiy, Y., Golian, V., Golian, N. (2013). Implementation of search mechanism for implicit dependences in process mining. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). doi:

Levykin, V., Chala, O. (2018). Method of automated construction and expansion of the knowledge base of the business process management system. EUREKA: Physics and Engineering, 4, 29–35. doi:



  • There are currently no refbacks.

Copyright (c) 2019 Serhii Chalyi, Inna Pribylnova

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)