METHOD OF FORMING RECOMMENDATIONS USING TEMPORAL CONSTRAINTS IN A SITUATION OF CYCLIC COLD START OF THE RECOMMENDER SYSTEM
Abstract
The problem of the formation of the recommended list of items in the situation of cyclic cold start of the recommendation system is considered. This problem occurs when building recommendations for occasional users. The interests of such consumers change significantly over time. These users are considered “cold” when accessing the recommendation system. A method for building recommendations in a cyclical cold start situation using temporal constraints is proposed. Temporal constraints are formed on the basis of the selection of repetitive pairs of actions for choosing the same objects at a given level of time granulation. Input data is represented by a set of user choice records. For each entry, a time stamp is indicated. The method includes the phases of the formation of temporal constraints, the addition of source data using these constraints, as well as the formation of recommendations using the collaborative filtering algorithm. The proposed method makes it possible, with the help of temporal constraints, to improve the accuracy of recommendations for “cold” users with periodic changes in their interests.
Downloads
References
Aggarwal, C. (2017). Recommender Systems. New York: Springer, 498.
Son, L. H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87–104. doi: http://doi.org/10.1016/j.is.2014.10.001
Bernardi, L., Kamps, J., Kiseleva, J., Müller, M. (2015). The Continuous Cold Start Problem in e-Commerce Recommender Systems. 2nd Workshop on New Trends on Content-Based Recommender Systems, 30–33.
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: http://doi.org/10.1109/idaacs.2013.6662657
Braunhofer, M. (2014). Hybrid Solution of the Cold-Start Problem in Context-Aware Recommender Systems. Lecture Notes in Computer Science, 484–489. doi: http://doi.org/10.1007/978-3-319-08786-3_44
Lika, B., Kolomvatsos, K., Hadjiefthymiades, S. (2014). Facing the cold start problem in recommender systems. Expert Systems with Applications, 41 (4), 2065–2073. doi: http://doi.org/10.1016/j.eswa.2013.09.005
Koren, Y. (2009). Collaborative Filtering with Temporal Dynamics. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 447–456. doi: http://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 Prefence Fusion. KDD’10 of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 723–732. doi: http://doi.org/10.1145/1835804.1835896
Elahi, M., Ricci, F., Rubens, N. (2016). A survey of active learning in collaborative filtering recommender systems. Computer Science Review, 20, 29–50. doi: http://doi.org/10.1016/j.cosrev.2016.05.002
Chalyi, S., Pribylnova, I. (2019). The Method of Constructing Recommendations Online on the Temporal Dynamics of User Interests Using Multilayer Graph. EUREKA: Physics and Engineering, 3, 13–19. doi: http://doi.org/10.21303/2461-4262.2019.00894
Luo, C., Cai, X., Chowdhury, N. (2014). Self-training Temporal Dynamic Collaborative Filtering. Lecture Notes in Computer Science, 461–472. doi: http://doi.org/10.1007/978-3-319-06608-0_38
Zhu, Y., Lin, J., He, S., Wang, B., Guan, Z., Liu, H., Cai, D. (2019). Addressing the Item Cold-start Problem by Attribute-driven Active Learning. IEEE Transactions on Knowledge and Data Engineering. doi: http://doi.org/10.1109/tkde.2019.2891530
Levykin, V., 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, 5 (3 (95)), 16–24. doi: http://doi.org/10.15587/1729-4061.2018.142664
Levykin, V., 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, 5, 3–10. doi: http://doi.org/10.21303/2461-4262.2018.00713
Chalyi, S., Levykin, I., Petrychenko, A., Bogatov, I. (2018). Causality-based model checking in business process management tasks. IEEE 9th International Conference on Dependable Systems, Services and Technologies DESSERT’2018. Kyiv, 478–483. doi: http://doi.org/10.1109/dessert.2018.8409176
Baltrunas, L., Ludwig, B., Ricci, F. (2011). Matrix factorization techniques for context aware recommendation. Proceedings of the Fifth ACM Conference on Recommender Systems, 301–304. doi: http://doi.org/10.1145/2043932.2043988
Copyright (c) 2019 Serhii Chalyi, Volodymyr Leshchynskyi, Irina Leshchynska
This work is licensed under a Creative Commons Attribution 4.0 International License.
Our journal abides by the Creative Commons CC BY copyright rights and permissions for open access journals.
Authors, who are published in this journal, agree to the following conditions:
1. The authors reserve the right to authorship of the work and pass the first publication right of this work to the journal under the terms of a Creative Commons CC BY, which allows others to freely distribute the published research with the obligatory reference to the authors of the original work and the first publication of the work in this journal.
2. The authors have the right to conclude separate supplement agreements that relate to non-exclusive work distribution in the form in which it has been published by the journal (for example, to upload the work to the online storage of the journal or publish it as part of a monograph), provided that the reference to the first publication of the work in this journal is included.
