Modelling and Data Analysis
2020. Vol. 10, no. 4, 17–30
doi:10.17759/mda.2020100402
ISSN: 2219-3758 / 2311-9454 (online)
Prediction the Probability of Purchases Recommended Items
Abstract
General Information
Keywords: recommendation systems, machine learning, binary classification, collaborative filtering methods, cosine similarity, map@K, recall@k
Journal rubric: Data Analysis
DOI: https://doi.org/10.17759/mda.2020100402
For citation: Parfenov P.A., Timofeeva A.A., Sologub G.B., Alekseychuk A.S. Prediction the Probability of Purchases Recommended Items. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2020. Vol. 10, no. 4, pp. 17–30. DOI: 10.17759/mda.2020100402. (In Russ., аbstr. in Engl.)
References
- Francesco Ricci and Lior Rokach and Bracha Shapira. Introduction to Recommender Systems Handbook// Springer Science+Business Media, LLC 2011, pp. 1–10.
- Mizzaro Stefano. Relevance: The Whole History // journal of the american society for information science, 1997, pp. 810–820.
- Brent Smith and Greg Linden. Two Decades of Recommender Systems at Amazon.com // the IEEE Computer Society, 2017, pp. 10–17.
- Carlos A. Gomez-Uribe and Neil Hunt. The Netflix Recommender System: Algorithms, Business Value, and Innovation // ACM Transactions on Management Information Systems, Vol. 6, No. 4, Article 13, 2015, pp. 6–7.
- E. Pyatikop. Study of the method of collaborative filtering based on the similarity of elements // Naukovi Pratsi DonNTU vipusk 2 (18), Series “Informatika, Kibernetika TA obchislyuvalna Tehnika”, 2013, pp. 109–110.
- Christopher D. Manning, Prabhakar Raghavan, Heinrich schütze. Introduction to information retrieval // Publishing house “Williams”, 2011, pp. 138.
- G. Litova, D.Y. Khanukaeva, Basics of vector algebra, Moscow, 2009, pp. 57.
- Jerome H. Friedman. Greedy Function Approximation: A Gradient Boosting Machine // Technical Discussion: Foundations of TreeNet(tm), 1999. P. 39.
- CatBoost [Electronic resource] // URL: https://neerc.ifmo.ru/wiki/index.php?title=CatBoost
- GridSearchCV [Electronic resource] // Scikit-learn URL: https://scikit-learn.org/stable/modules/ generated/sklearn.model_selection.GridSearchCV.html
- Gunnar Schröder, Maik Thiele, Wolfgang Lehner. Setting Goals and Choosing Metrics for Recommender System Evaluations, 2011 P. 8.
- Ziwei Zhu, Jianling Wang, James Caverlee // Improving Top-K Recommendation via Joint Collaborative Autoencoders, IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4 License, 2019. P. 7.
- CatBoost Feature Importance [Electronic resource] // catboost URL: https://catboost.ai/docs/ concepts/fstr.html#fstr
- Wen Zhang, Taketoshi Yoshida, Xijin Tang. A comparative study of TFIDF, LSI and multi-words for text classification // Expert Systems with Applications, 2010. 8.
- Tom Fawcett. An introduction to ROC analysis // Pattern Recognition Letters 27, 2006. 865.
Information About the Authors
Metrics
Views
Total: 388
Previous month: 8
Current month: 2
Downloads
Total: 281
Previous month: 4
Current month: 1