IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System
Recommendation systems are becoming essential components of contemporary online goods and services, and they significantly affect customer satisfaction. Recommendation systems are designed to empower customers in their decision-making process by providing personalized recommendations. Meta-learning...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10977006/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850232520330706944 |
|---|---|
| author | Nida Siddique Amna Zafar Beenish Ayesha Akram Muhammad Waseem Sajid Iqbal Ahmad A. Al-Yahya Muhammad Nabeel Asghar Abdullah Abdulrrehman Alaulamie |
| author_facet | Nida Siddique Amna Zafar Beenish Ayesha Akram Muhammad Waseem Sajid Iqbal Ahmad A. Al-Yahya Muhammad Nabeel Asghar Abdullah Abdulrrehman Alaulamie |
| author_sort | Nida Siddique |
| collection | DOAJ |
| description | Recommendation systems are becoming essential components of contemporary online goods and services, and they significantly affect customer satisfaction. Recommendation systems are designed to empower customers in their decision-making process by providing personalized recommendations. Meta-learning has proven to be effective in addressing user cold-start problems in recommendation systems. Many meta-learning-based recommendation systems designed for the cold-start problem are gradient-based. The existing frameworks require optimization techniques to maximize the potential of hyper networks, enabling them to adapt and generate compatible parameters for efficient learning as meta-learners. However, these frameworks often lack contextual knowledge from users to provide suitable initial guidelines in the recommendation network for new users. We propose a meta-learning-based framework that improves cold-start recommendation accuracy by incorporating user behavior and preferences. Evaluations on MovieLens 100K and DBook datasets show our IMETA-GNN model outperforms state-of-the-art baselines, achieving accuracies of 82% and 75%, respectively. Performance evaluation demonstrates that our proposed method outperforms several state-of-the-art metalearning recommendation systems in addressing the user cold-start conundrum. |
| format | Article |
| id | doaj-art-5c3f9bc7c6094161abe7efb7516efef4 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5c3f9bc7c6094161abe7efb7516efef42025-08-20T02:03:08ZengIEEEIEEE Access2169-35362025-01-0113939649397610.1109/ACCESS.2025.356445410977006IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation SystemNida Siddique0Amna Zafar1https://orcid.org/0000-0002-7270-7238Beenish Ayesha Akram2https://orcid.org/0000-0002-5348-9132Muhammad Waseem3https://orcid.org/0000-0001-9605-2531Sajid Iqbal4https://orcid.org/0000-0002-8464-2275Ahmad A. Al-Yahya5Muhammad Nabeel Asghar6https://orcid.org/0000-0002-9487-4344Abdullah Abdulrrehman Alaulamie7Department of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science, University of Engineering and Technology, Lahore, PakistanDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Alahsa, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Alahsa, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Alahsa, Saudi ArabiaDepartment of Information Systems, College of Computer Science and Information Technology, King Faisal University, Alahsa, Saudi ArabiaRecommendation systems are becoming essential components of contemporary online goods and services, and they significantly affect customer satisfaction. Recommendation systems are designed to empower customers in their decision-making process by providing personalized recommendations. Meta-learning has proven to be effective in addressing user cold-start problems in recommendation systems. Many meta-learning-based recommendation systems designed for the cold-start problem are gradient-based. The existing frameworks require optimization techniques to maximize the potential of hyper networks, enabling them to adapt and generate compatible parameters for efficient learning as meta-learners. However, these frameworks often lack contextual knowledge from users to provide suitable initial guidelines in the recommendation network for new users. We propose a meta-learning-based framework that improves cold-start recommendation accuracy by incorporating user behavior and preferences. Evaluations on MovieLens 100K and DBook datasets show our IMETA-GNN model outperforms state-of-the-art baselines, achieving accuracies of 82% and 75%, respectively. Performance evaluation demonstrates that our proposed method outperforms several state-of-the-art metalearning recommendation systems in addressing the user cold-start conundrum.https://ieeexplore.ieee.org/document/10977006/Cold start problemmeta-learningrecommendation systemGNNoptimization |
| spellingShingle | Nida Siddique Amna Zafar Beenish Ayesha Akram Muhammad Waseem Sajid Iqbal Ahmad A. Al-Yahya Muhammad Nabeel Asghar Abdullah Abdulrrehman Alaulamie IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System IEEE Access Cold start problem meta-learning recommendation system GNN optimization |
| title | IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System |
| title_full | IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System |
| title_fullStr | IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System |
| title_full_unstemmed | IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System |
| title_short | IMETA-GNN: Meta Learning-Based Cold Start Optimization for Recommendation System |
| title_sort | imeta gnn meta learning based cold start optimization for recommendation system |
| topic | Cold start problem meta-learning recommendation system GNN optimization |
| url | https://ieeexplore.ieee.org/document/10977006/ |
| work_keys_str_mv | AT nidasiddique imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT amnazafar imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT beenishayeshaakram imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT muhammadwaseem imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT sajidiqbal imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT ahmadaalyahya imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT muhammadnabeelasghar imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem AT abdullahabdulrrehmanalaulamie imetagnnmetalearningbasedcoldstartoptimizationforrecommendationsystem |