<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new application, the proposed system will present a better technique that focuses especially on resolving the cold start issue. The suggested system will compute similarity using the Pearson Correlation Coefficient (PCC). Collaborative filtering that uses PCC suffers from the cold start problem or a lack of information on new users to generate useful recommendations. The proposed system solves the issue of cold start by gauging each new user by certain arbitrary parameters and recommending based on the choices of other users in that demographic. The proposed system also solves the issue of users’ reluctance to provide ratings by implementing a keyword-based perception system that will aid users in finding the right product for them.</p>
</abstract>

One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s discussion of product suggestions. In addition to utilizing collaborative filtering in a new application, the proposed system will present a better technique that focuses especially on resolving the cold start issue. The suggested system will compute similarity using the Pearson Correlation Coefficient (PCC). Collaborative filtering that uses PCC suffers from the cold start problem or a lack of information on new users to generate useful recommendations. The proposed system solves the issue of cold start by gauging each new user by certain arbitrary parameters and recommending based on the choices of other users in that demographic. The proposed system also solves the issue of users’ reluctance to provide ratings by implementing a keyword-based perception system that will aid users in finding the right product for them.

An Improved Product Recommender System Using Collaborative Filtering and a Comparative Study of ML Algorithms

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

One of the methods most frequently used to recommend films is collaborative filtering. We examine the potential of collaborative filtering in our paper’s...

An Improved Product Recommender System Using Collaborative Filtering and a Comparative Study of ML Algorithms

Publié en ligne: 30 nov. 2023

Pages: 51 - 62

Reçu: 02 mai 2023

Accepté: 17 nov. 2023

DOI: https://doi.org/10.2478/cait-2023-0035

Mots clés
Recommender system, Collaborative Filtering (CF), Pearson Correlation Coefficient (PCC), Cold start problem, Data sparsity

© 2023 S. Amutha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Improved Product Recommender System Using Collaborative Filtering and a Comparative Study of ML Algorithms

Publié en ligne: 30 nov. 2023

Pages: 51 - 62

Reçu: 02 mai 2023

Accepté: 17 nov. 2023

DOI: https://doi.org/10.2478/cait-2023-0035

Mots clésRecommender system, Collaborative Filtering (CF), Pearson Correlation Coefficient (PCC), Cold start problem, Data sparsity

© 2023 S. Amutha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
Recommender system, Collaborative Filtering (CF), Pearson Correlation Coefficient (PCC), Cold start problem, Data sparsity