COLLABORATIVE FILTERING AS ONE OF THE MAIN METHODS OF CONTENT RECOMMENDATIONS: MAIN PROBLEMS AND WAYS TO SOLVE

Authors

DOI:

https://doi.org/10.30890/2567-5273.2024-34-00-013

Keywords:

collaborative filtering, "synonymy” of objects "cold start”, data sparsity, cluster analysis.

Abstract

The article considers collaborative filtering as one of the main methods of content recommendation. The main problems of the method are analyzed, such as "synonymy of objects", "cold start", sparsity of data. The ways to solve these problems by using the

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References

Brangbour Etienne, Bruneau Pierrick, Tamisier Thomas, Marchand-Maillet Stephane. Active Learning with Crowdsourcing for the Cold Start of Imbalanced Classifiers. Cooperative Design, Visualization, and Engineering. 2020. P .192–201. URL: https://www.researchgate.net/publication/346246697_Active_Learning_with_Crowdsourcing_for_the_Cold_Start_of_Imbalanced_Classifiers

Deerwester Scott, Dumaism Susan T., Furnas George W., Landauer Thomas K., Harshman Richard. Indexing by Latent Semantic Analysis. URL: http://wordvec.colorado.edu/papers/Deerwester_1990.pdf

Dias Charles-Emmanuel, Guigue Vincent, Gallinari Patrick. Text-based collaborative filtering for coldstart soothing and recommendation enrichment. AISR2017, May 2017, Paris, France. URL: https://hal.science/hal-01640268/document.

Ghabayen Ayman S., Noah Shahrul Azman. Using Tags for Measuring the Semantic Similarity of Users to Enhance Collaborative Filtering Recommender Systems. International Journal on Advanced Science Engineering and Information Technology. 2017. P. 2063-2070. URL: https://www.researchgate.net/profile/Ayman-Ghabayen-2/publication/320258585_Using_Tags_for_Measuring_the_Semantic_Similarity_of_Users_to_Enhance_Collaborative_Filtering_Recommender_Systems/links/5a423075458515f6b04dd899/Using-Tags-for-Measuring-the-Semantic-Similarity-of-Users-to-Enhance-Collaborative-Filtering-Recommender-Systems.pdf .

Lafia Neal, Capra Licia, Hailes Stephen. Temporal Collaborative Filtering With Adaptive Neighbourhoods. Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR 2009. Boston. USA. 2009. P.796-797. URL: https://doi.org/10.1145/1571941.1572133.

Liang, Huizhi and Xu, Yue and Li, Yuefeng and Nayak, Richi (2009) Collaborative Filtering Recommender Systems based on Popular Tags. Proceedings of the Fourteenth Australasian Document Computing Symposium, 4 December 2009, University of New South Wales, Sydney. P. 1-8. URL: https://eprints.qut.edu.au/29732/1/29732.pdf .

Liang, Huizhi, Xu, Yue, Li, Yuefeng, & Nayak, Richi. Collaborative filtering recommender systems based on popular tags. Proceedings of the 14th Australasian Document Computing Symposium. University of Sydney. Australia. 2009. Р. 1-8. URL: https://eprints.qut.edu.au/29732/1/29732.pdf .

Lui Nathannan, Zhao Min, Xiang Evanwei, Yang Qiang. Online evolutionary collaborative filtering. Proceedings of the fourth ACM conference on Recommender systems. 2010. P. 95-102. URL: https://doi.org/10.1145/1864708.1864729.

Nasraoui Olfa, Cerwinske Jeff, Rojas Carlos, and Gonzalez Fabio. Performance of Recommendation Systems in Dynamic Streaming Environments. Proceedings of the 2007 SIAM International Conference on Data Mining. P. 569-574. URL: https://doi.org/10.1137/1.9781611972771.63

O’Connor Mark, Herlocker Jon Clustering Items for Collaborative Filtering. https://redirect.cs.umbc.edu/~ian/sigir99-rec/papers/oconner_m.pdf

Qin Lui A New Collaborative Filtering Algorithm Integrating Time and Multisimilarity. Mathematical Problems in Engineering. 2022. URL: https://www.hindawi.com/journals/mpe/2022/2340671/

Ungar Lyle H. and Foster. Dean P. Clustering Methods for Collaborative Filtering Technical Report. 1998. Р. 114–128. URL: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=d6b99637332f818dc94c0432db617db28873e035.

Yiwei Cao, Klamma Ralf. Clustering Тechnique for Collaborative Filtering and the Application to Venue Recommendation. Proceeding of the 10th International Conference on Knowledge Management and Knowledge Technologies (I-KNOW 2010), 1-3 September, 2010, Graz, Austria URL: https://www.researchgate.net/publication/228446479_Clustering_Technique_for_Collaborative_Filtering_and_the_Application_to_Venue_Recommendation

Yi Ding, Xue Li. Time Weight Collaborative Filtering. URL: https://cseweb.ucsd.edu/classes/fa17/cse291-b/reading/p485-ding.pdf

Yi Ding, Xue Li, Мaria Orlowska.Recency-based collaborative filtering. Proceedings of the 17th Australasian Database Conference. 2006. Hobart. Tasmania. Australia. January 16–19. 2006. URL: https://www.researchgate.net/publication/221152610_Recency-based_collaborative_filtering.

Івохін Є., Шелякін Г., Махно М. Удосконалення методу колаборативної фільтрації шляхом інтегрування семантичного та часового факторів і методу кластерного аналізу. Artificial Intelligence. №1, 2024. С. 57 – 63. URL: https://jai.in.ua/archive/2024/2024-1-5.pdf

Published

2024-08-30

How to Cite

Івохін, Є., & Шелякін, Г. (2024). COLLABORATIVE FILTERING AS ONE OF THE MAIN METHODS OF CONTENT RECOMMENDATIONS: MAIN PROBLEMS AND WAYS TO SOLVE. Modern Engineering and Innovative Technologies, 1(34-01), 94–105. https://doi.org/10.30890/2567-5273.2024-34-00-013

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Articles