- PII
- S30346444S0002338825050104-1
- DOI
- 10.7868/S3034644425050104
- Publication type
- Article
- Status
- Published
- Authors
- Volume/ Edition
- Volume / Issue number 5
- Pages
- 125-140
- Abstract
- Modern recommender systems increasingly go beyond classical personalization tasks, addressing more complex scenarios of interactions between items. One such challenge is generating complementary recommendations, where standard user-centric architectures often lack sufficient flexibility. This study compares two matrix factorization-based approaches to solving this problem: a classical model trained on the user–item matrix with additional constraints derived from co-occurrence statistics, and a direct factorization of an item–item matrix constructed using a temporal co-action rule. The paper analyzes ways to overcome the limitations of traditional methods and outlines the potential of new strategies across various data types and business applications.
- Keywords
- рекомендательные системы комплементарные товары матричная факторизация пользователи объекты эмбеддинги
- Date of publication
- 09.12.2025
- Year of publication
- 2025
- Number of purchasers
- 0
- Views
- 27
References
- 1. McAuley J., Pandey R., Leskovec J. Inferring Networks of Substitutable and Complementary Products // arXiv:1506.08839, 2015.
- 2. Shi Y., Larson M., Hanjalic A. Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification // Frontiers in Big Data. 2020. V. 2. № 49.
- 3. Deshpande M., Karypis G. Item-based top-N Recommendation Algorithms // ACM Transactions on Information Systems (TOIS). 2004. V. 22. № 1. P. 143–-177.
- 4. Sinha L., Sinha N. Personalized Diversification of Complementary Recommendations with User Preference in Online Grocery // Frontiers in Big Data. 2023. V. 6.
- 5. Rendle S., Krichene W., Zhang L., Anderson J. Neural Collaborative Filtering vs. Matrix Factorization Revisited // Proc. 14th ACM Conf. on Recommender Systems. Brazil, 2020. P. 240–248.
- 6. Zharova M., Tsurkov V. Boosting Based Recommender System // J. Computer and Systems Sciences International. 2024. V. 63. P. 922–940
- 7. Zharova M., Tsurkov V. Neural Network Approaches for Recommender Systems // J. Computer and Systems Sciences International. 2024. V. 62. P. 1048–1062.
- 8. Zhou Y., Wilkinson D., Schreiber R., Pan R. Large-Scale Parallel Collaborative Filtering for the Netflix Prize // Proc. Intern. Conf. on Algorithmic Applications in Management (AAIM). Shanghai, China, 2008. P. 337–348.
- 9. Al-Nafjan A., Alrashoudi N., Alrasheed H. Recommendation System Algorithms on Location-Based Social Networks: Comparative Study // Information. 2022. V. 13. № 188.
- 10. Guan X., Li C.-T. Enhanced SVD for Collaborative Filtering // Advances in Knowledge Discovery and Data Mining. 2016. V. 9652. P. 503–514
- 11. Cacheda F., Carneiro V. Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-performance Recommender Systems // ACM Transactions on the Web (TWEB). 2011. V. 5. № 1. P. 1–33.
- 12. Hu Y., Koren Y., Volinsky C. Collaborative Filtering for Implicit Feedback Datasets // Proc. Intern. Conf. on Data Mining (ICDM). Pisa, Italy, 2008. P. 263–272
- 13. McDonald G. C. Ridge Regression // Wiley Interdisciplinary Reviews: Computational Statistics. 2009. V. 1. № 1. P. 93–100.
- 14. Имплементация ALS на Python // GitHub. Implicit: Webcite https://github.com/benfred/implicit (accessed: 10.07.2025).
- 15. Tas K. On the Implicit Feedback Based Data Modeling Approaches for Recommendation Systems // Proc. Intern. Conf. on Electrical, Communication, and Computer Engineering (ICECCE). Kuala Lumpur, Malaysia, 2021. P. 1–6.
- 16. Rendle S., Freudenthaler C., Gantner Z., Schmidt-Thieme L. BPR: Bayesian Personalized Ranking from Implicit Feedback // Proc. Conf. on Uncertainty in Artificial Intelligence. Montreal Quebec, Canada, 2009. P. 452–461.
- 17. Zharova M., Tsurkov V. Using Negative Actions for Improving Quality of Recommender Systems // J. Computer and Systems Sciences International. 2025. V. 64. P. 132–148.
- 18. Ding J., Yu G., He X., Li Y. Sampler Design for Bayesian Personalized Ranking by Leveraging View Data // arXiv:1809.08162, 2018.
- 19. Kula M. Metadata Embeddings for User and Item Cold-start Recommendations // arXiv:1507.08439, 2018.
- 20. Berisha F Addressing Cold Start in Recommender Systems with Neural Networks: A Literature Survey // Intern. J. of Computers and Applications. 2023. V. 45. P. 485–496.
- 21. Zhang Z., Shi L., Zhou D.-X. Classification with Deep Neural Networks and Logistic Loss // J. Machine Learning Research (JMLR). 2024. V. 25. P. 1–117.
- 22. Weston J., Bengio S., Usunier N. Scaling Up To Large Vocabulary Image Annotation // Proc. Intern. Joint Conf. on Artificial Intelligence. Barcelona, Spain, 2011. P. 2764–2770.
- 23. Имплементация библиотеки LightFM // GitHub. LightFM: webcite https://github.com/lyst/lightfm (accessed: 10.07.2025).
- 24. He X., Liao L., Zhang H., Nie L., Hu X., Chua T.-S. Neural Collaborative Filtering // Proc. Intern. Conf. on World Wide Web. Perth. Australia, 2017. P. 173–182.
- 25. Hurtik P., Tomasiello S. Binary Cross-entropy with Dynamical Clipping // Neural Computing and Applications. 2022. V. 34. № 14. P. 12029–12041.
- 26. Ayub M. An Effective Model for Jaccard Coefficient to Increase the Performance of Collaborative Filtering // Arabian J. for Science and Engineering. 2020. V. 45. P. 9997–10017.
- 27. Lobo D. Association Rules: Normalizing the Lift // Proc. Intern. Conf. on Digital Information Management (ICDIM). Phitsanulok, Thailand, 2014. P. 151–155.
- 28. Pellegrini R. Don’t Recommend the Obvious: Estimate Probability Ratios // Proc. ACM Recommender Systems Conf. (RecSys). Seattle, USA, 2022. P. 188–197.
- 29. Chowdhury P. Evaluating the Effectiveness of Collaborative Filtering Similarity Measures: A Comprehensive Review // Procedia Computer Science. 2024. V. 235. P. 2641–2650.
- 30. Li M. Modeling Personalized Representation for Within-Basket Recommendation // Expert Systems with Applications. 2022. V. 192. Iss. C.
- 31. Имплементация Faiss для Python // GitHub. Faiss-wheels: webcite https://github.com/kyamagu/faiss-wheels (accessed: 10.07.2025).
- 32. Douze M., Guzhva A., Deng C. The Faiss Library // arXiv:2401.08281, 2025.
- 33. Amati G. BM25 // In Encyclopedia of Database Systems. Boston, MA: Springer, 2009. P. 257–260.
