Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

Celestine Iwendi (), Ebuka Ibeke (), Harshini Eggoni (), Sreerajavenkatareddy Velagala () and Gautam Srivastava
Additional contact information
Celestine Iwendi: School of Creative Technologies, University of Bolton, UK
Ebuka Ibeke: School of Creative and Cultural Business, Robert Gordon University, Aberdeen, Scotland, UK
Harshini Eggoni: Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore 560300, India
Sreerajavenkatareddy Velagala: Department of Computer Science and Engineering, Nagarjuna College of Engineering and Technology, Bangalore 560300, India
Gautam Srivastava: Department of Mathematics and Computer Science, Brandon University, MB, Canada5Research Centre for Interneural Computing, China Medical University, Taiching, Taiwan

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 01, 463-484

Abstract: The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with 79% accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of 21%, precision at 79%, recall at 80% and F1-Score at 79%. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

Keywords: Recommender systems; keyword-item recommendation; machine learning; collaborative filtering; rating (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219622021500619

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