Genetically Optimized GAT Recommender

Title

Genetically Optimized GAT Recommender

Creator

Vikash Krishna, R.; Sundharess, B.; Sudhakar, T.; Donald, Cecil; Joy, Helen K.; Shanthan, Hubert

Description

In the digital era of business growth in areas such as e-commerce, online food tech, Health tech, Edutech, etc., A significant issue encountered by businesses to customers (B2C) companies is the recommendation of products, particularly for cold start users. The research proposes a recommendation system using Graph Attention(GAT)-based architecture with a genetically optimized clustering algorithm, which solves the general and cold start users problem in a recommendation. In order to solve the general recommendation problems, the proposed architecture is applied to a benchmark dataset from Amazon, with a recommendation accuracy of 99% and for cold start users, the Proposed model will add an extra layer of personalization as a survey will be included during the registration process of the user. The dataset for the survey was collected using Google Forms, and the dataset comprises five primary attributes: name, gender, user type, interested domains, interested products of new users, and recommended products of old users. A clustering analysis was performed on the dataset, and DBscan was used with genetic optimization as it outmatched other clustering algorithms. Then, transfer learning was applied to the survey collected with the same architecture, and it achieved an accuracy of 79.52% with an MSE loss of 0.0664. 2025 IEEE.

Source

2025 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Clustering; Cold-start problem; GNN; Recommendation System; User experience; User profiling

Coverage

Vikash Krishna R., Christ University, Department of Computer Science, Bangalore, India; Sundharess B., Christ University, Department of Computer Science, Bangalore, India; Sudhakar T., Christ University, Department of Computer Science, Bangalore, India; Donald C., Christ University, Department of Computer Science, Bangalore, India; Joy H.K., Christ University, Department of Computer Science, Bangalore, India; Shanthan H., Christ University, Department of Computer Science, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833150236-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/CONECCT65861.2025.11306790

https://www.scopus.com/pages/publications/105032700431?origin=resultslist