THE METHOD OF CONSTRUCTING RECOMMENDATIONS ONLINE ON THE TEMPORAL DYNAMICS OF USER INTERESTS USING MULTILAYER GRAPH

Serhii Chalyi, Inna Pribylnova

Abstract


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.


Keywords


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

Full Text:

PDF

References


Ricci, F., Rokach, L., Shapira, B. (Eds.) (2015). Recommender Systems. Springer, 1008. doi: https://doi.org/10.1007/978-1-4899-7637-6

Amatriain, X. (2013). Mining large streams of user data for personalized recommendations. ACM SIGKDD Explorations Newsletter, 14 (2), 37–48. doi: https://doi.org/10.1145/2481244.2481250

Aggarwal, C. (2017). Recommender Systems. Springer, 498. doi: https://doi.org/10.1007/978-3-319-29659-3

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: https://doi.org/10.1145/2501221.2501222

Jones, M. T. (2013). Recommender systems, Part 1. Introduction to approaches and algorithms. Available at: https://www.ibm.com/developerworks/opensource/library/os-recommender1/index.html?S_TACT=105AGX99&S_CMP=CP

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: https://doi.org/10.1145/336992.337035

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: https://doi.org/10.1145/1557019.1557072

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: https://doi.org/10.1145/1835804.1835896

Luo, C., Cai, X., Chowdhury, N. (2014). Self-training Temporal Dynamic Collaborative Filtering. Lecture Notes in Computer Science, 461–472. doi: https://doi.org/10.1007/978-3-319-06608-0_38

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: https://doi.org/10.17559/tv-20160708140839

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: https://doi.org/10.1109/dessert.2018.8409176

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: https://doi.org/10.1109/idaacs.2013.6662657

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: https://doi.org/10.21303/2461-4262.2018.00676




DOI: http://dx.doi.org/10.21303/2461-4262.2019.00894

Refbacks

  • 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)