Using Feature Selection Methods to Discover Common Users’ Preferences for Online Recommender Systems
Recommender systems have taken over user’s choice to choose the items/services they want from online markets, where lots of merchandise is traded. Collaborative filtering-based recommender systems uses user opinions and preferences. Determination of commonly used attributes that influence preferences used for prediction and subsequent recommendation of unknown or new items to users is a significant objective while developing recommender engines. In conventional systems, study of user behavior to know their dis/like over items would be carried-out. In this paper, presents feature selection methods to mine such preferences through selection of high influencing attributes of the items. In machine learning, feature selection is used as a data pre-processing method but extended its use on this work to achieve two objectives; removal of redundant, uninformative features and for selecting formative, relevant features based on the response variable. The latter objective, was suggested to identify and determine the frequent and shared features that would be preferred mostly by marketplace online users as they express their preferences. The dataset used for experimentation and determination was synthetic dataset. The Jupyter Notebook™ using python was used to run the experiments. Results showed that given a number of formative features, there were those selected, with high influence to the response variable. Evidence showed that different feature selection methods resulted with different feature scores, and intrinsic method had the best overall results with 85% model accuracy. Selected features were used as frequently preferred attributes that influence users’ preferences.
R. Obeidat, R. Duwairi, A. Al-Aiad. A collaborative recommendation system for online courses recommendations. In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) 2019 Aug 26 (pp. 49-54). IEEE.
P. Kumar, V. Kumar, RS. Thakur. A new approach for rating prediction system using collaborative filtering. Iran Journal of Computer Science. 2019 Jun 1;2(2): pp 81-7.
M. K. Najafabadi, A. H. Mohamed, & M. N. Mahrin. (2019). A survey on data mining techniques in recommender systems. Soft Computing. 2019 Jan 30;23(2):627-54.
C. C. Aggarwal. Recommender systems. Cham: Springer International Publishing; 2016.
X. Y. Liu, Y. Liang, S. Wang, Z.Y. Yang, H.S. Ye. A hybrid genetic algorithm with wrapper-embedded approaches for feature selection. IEEE Access. 2018 Mar 27;6:22863-74.
I. D. Dinov. Data science and predictive analytics: Biomedical and health applications using R. Springer; 2018 Aug 27.
J. Brownlee. How to choose a feature selection method for machine learning. Machine Learning Mastery. 2019 Dec;10.
J. Brownlee. Information gain and mutual information for machine learning. Preuzeto. 2019;18:2020.
M. Kuhn, K. Johnson. Applied predictive modeling. New York: Springer; 2013 Sep.
B. Venkatesh, J. Anuradha. A review of feature selection and its methods. Cybernetics and Information Technologies. 2019 Mar 1;19(1):3-26.
V. Bolón-Canedo, A. Alonso-Betanzos. Ensembles for feature selection: A review and future trends. Information Fusion. 2019 Dec 1;52:1-2.
S. Kaushik. Introduction to feature selection methods with an example.
A. Bommert, X. Sun, B.Bischl, J. Rahnenführer, M. Lang. Benchmark for filter methods for feature selection in high-dimensional classification data. Computational Statistics & Data Analysis. 2020 Mar 1;143: pp 106839.
T. Hamed. Recursive feature addition: A novel feature selection technique, including a proof of concept in network security (Doctoral dissertation).
G. Borboudakis, I. Tsamardinos. Forward-backward selection with early dropping. The Journal of Machine Learning Research. 2019 Jan 1;20(1): pp276-314.
R. H. Huang, T. H.Yu. An effective ant colony optimization algorithm for multi-objective job-shop scheduling with equal-size lot-splitting. Applied Soft Computing. 2017 Aug 1;57: pp 642-56.
Y. B. Wah, N. Ibrahim, H. A. Hamid, S. Abdul-Rahman, S. Fong. Feature selection methods: case of filter and wrapper approaches for maximising classification accuracy. Pertanika Journal of Science & Technology. 2018 Jan 1;26(1).
C. Xu. Common methods for feature selection you should know. https://medium.com/@cxu24/common-methods-for-feature-selection-you-should-know-2346847fdf31#. 2018, June 18.
B. C. Boehmke. Data wrangling with R. Springer International Publishing. 2016.
H. K. Dam, T. Tran, A. Ghose. Explainable software analytics. In Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results 2018 May 27 pp. 53-56.
A. Bahl, B. Hellack, M. Balas, A. Dinischiotu, M. Wiemann, J. Brinkmann, A. Luch, B. Y. Renard, A. Haase. Recursive feature elimination in random forest classification supports nanomaterial grouping. NanoImpact. 2019 Mar 1;15: p 100179.
How to Cite
Copyright (c) 2021 Rachael Njeri Ndung'u, Gabriel Ndung’u Kamau , Geoffrey Wambugu Mariga
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.