IMPLEMENTATION OF A HYBRID METHOD OF SEARCHING FOR CLOSE OBJECTS, TAKING INTO ACCOUNT THE GENERAL AND ACOUSTIC CHARACTERISTICS
DOI:
https://doi.org/10.30837/ITSSI.2021.15.059Keywords:
audio characteristics, recommendation system, collaborative filtering, content oriented methodAbstract
The subject of research in the article is the methods of finding close objects and technologies of forming recommendations. The aim of the article is to develop a recommendation system based on a hybrid method of searching for objects, taking into account both user preferences and audio characteristics of objects. The following tasks are solved: analysis of methods and algorithms used in recommendation systems; development of a hybrid method of forming recommendations on the principle of double organization; determination of the main functions and architecture of the system of formation of musical recommendations; testing of calculation algorithms and search methods in the system for analysis of similarity of musical recommendations. The following research methods are used: methods of correlation analysis, methods of similarity theory, algorithms of collaborative filtering and content analysis, hybrid methods, methods of analysis of audio characteristics, programming technologies. The following results were obtained: A study of collaborative filtering, content-based filtering and hybrid methods. Algorithms and calculation formulas of the considered methods are given. The main audio characteristics of musical compositions are considered. The method of formation of recommendations on the principle of double organization is developed. The main functions of the system of formation of musical recommendations are listed and the diagram of components is formed. An example of calculating the characteristics of user preferences and similarity of musical compositions by audio characteristics is given. Conclusions: According to the results of testing the system by three methods, we can conclude that the proposed hybrid method was the most effective among the studied recommendation methods with the lowest standard error rate. In addition, the hybrid method on the principle of double organization solves such problems of existing recommendation methods as excessive similarity of recommendations, potentially small number or no proposals at all by compensating data from one block of data from another.
References
Haidai, B., Artiukh, R., Malyeyeva, O. (2018), "Analysis and modelling the preferences of social networks users", Innovative Technologies and Scientific Solutions for Industries, No. 1 (3), P. 5–12. DOI: http://dx.doi.org/10.30837/2522-9818.2018.3.005
Aggarwal, C. C. (2017), Recommender Systems : The Textbook, Springer, New York, 498 p.
Miller, B. N., Konstan, J. A., Riedl, J. (2004) "PocketLens: toward a personal recommender system", ACM Transactions on Information Systems, Vol. 22, No. 3, P. 437–476.
Chalyy, S. F., Leshchynsʹkyy, V. O., Leshchynsʹka, I. O. (2018) "Modelyuvannya kontekstu v rekomendatsiynykh systemakh". Problems of information technologies, No. 23, P. 21–26. DOI: https://doi.org/10.35546/піт.v0i23.193
Malyeyeva, O., Nosova, N., Fedorovych, O., Kosenko, V. (2018) "The semantic network creation for an innovative project scope as a part of project knowledge ontology", CEUR Workshop Proceedings, P. 2362.
Martín, S. S., López-Catalán, B., Ramón-Jerónimo, M. A. (2012), "Factors determining firms’ perceived performance of mobile commerce", Industrial Management & Data Systems, No. 112, P. 946–963.
Meleshko, Е. V. Semenov, S. G. Khokh, V. D. (2018), "Doslidzhennya metodiv pobudovy rekomendatsiynykh system v merezhi internet", Control, Navigation and Communication Systems. Collection of Scientific Papers, No. 1 (47), P. 131–136. DOI: https://doi.org/10.26906/SUNZ.2018.1.131
Rohushyna, Yu. V. (2014), "Rozrobka metodiv formuvannya ta popovnenya ontolohichnoyi modeli semantichnoyi poshucovo-rekomenduyushoyi systemy", Engineering Software, No. 2 (18), P. 34–46.
Baltrunas, L., Ludwig, B., Peer, S., Ricci, F. (2011), "Context-Aware Places of Interest Recommendations for Mobile Users", Proceedings of the 14th International Conference on Human-Computer Interaction, Berlin: Springer, P. 531–540. DOI: https://doi.org/10.1007/978-3-642-21675-6_61
Baltrunas, L., Ludwig, B., Ricci, F. (2011), "Context Relevance Assessment for Recommender Systems", Proceedings of the 16th International Conference on Intelligent User Interfaces, New York : Association for Computing Machinery, P. 287–290. DOI: https://doi.org/10.1145/1943403.1943447
Xiaoyuan, Su, Taghi, M., Khoshgoftaar, A. (2009), "Survey of Collaborative Filtering Techniques A Survey of Collaborative Filtering Techniques", Advances in Artificial Intelligence Archive, Article ID 421425, 19 p. DOI: https://doi.org/10.1155/2009/421425
Herlocker, J. L., Konstan, J. A., Terveen, L. G. Riedl, J. T. (2004), "Evaluating collaborative filtering recommender systems", ACM Transactions on Information Systems (TOIS), Vol. 22, No. 1, P. 5–53.
Karypis, G. (2001), "Evaluation of item-based top-N recommendation algorithms", CIKM '01: Proceedings of the 10th international conference on Information and knowledge management, P. 247–254. DOI: https://doi.org/10.1145/502585.502627
Kucheruk, V. Yu., Hlushko, M. V. (2018), "Pokrashchennya alhorytmu "ITEM TO ITEM" metodu kolaboratyvnoyi filʹtratsiyi dlya rozrobky rekomendatsiynykh system na osnovi kosynusnoyi miry shlyakhom otsinky relevantnosti", Scientific Journal "ScienceRise", Vol. 1, No. 1 (42), P. 20–24. DOI: https://doi.org/10.15587/2313-8416.2018.120886
Malieieva, J., Kosenko, V., Malyeyeva, O., Svetlichnyj, D. (2019), "Creation of collaborative development environment in the system of distance learning", Innovative Technologies and Scientific Solutions for Industries, No. 2 (8), P. 62–71. DOI: https://doi.org/10.30837/2522-9818.2019.8.062
Miyahara, K., Pazzani, M. J. (2002), "Improvement of collaborative filtering with the simple Bayesian classifier", Semantic Scholar, Corpus ID: 16843019.
"Content-based book recommendation using learning for text categorization", available at : https://www.cs.utexas.edu/users/ml/papers/libra-sigir-wkshp-99.pdf
Sobhanam, H., Mariappan, A. K. (2013), "Addressing cold start problem in recommender systems using association rules and clustering technique", Proceedings of the International Conference on Computer Communication and Informatics (ICCCI- 2013), P. 402–411.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Our journal abides by the Creative Commons copyright rights and permissions for open access journals.
Authors who publish with this journal agree to the following terms:
Authors hold the copyright without restrictions and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors are able to enter into separate, additional contractual arrangements for the non-commercial and non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
Authors are permitted and encouraged to post their published work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as earlier and greater citation of published work.
