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Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P103 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P103

Adaptive Graph Contrastive Multi-Criteria Recommender with Dynamic Preference Evolution

K. Sravanthi, T. Archana

Received Revised Accepted Published
06 Mar 2026 05 Apr 2026 04 May 2026 27 Jun 2026

Citation :

K. Sravanthi, T. Archana, "Adaptive Graph Contrastive Multi-Criteria Recommender with Dynamic Preference Evolution," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 28-37, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P103

Abstract

The increasing amount of online information has resulted in information overload, leading to the need for precise and adaptive recommenders. The existing single-criterion recommenders fail to address the multidimensional nature of user preferences, whereas the existing Multi-Criteria Recommender Systems (MCRS) face the problems of sparsity, limited relational modeling, and static assumptions of user preferences. This paper presents a novel Temporal Graph Contrastive Deep Autoencoder-based Multi-Criteria Recommender System (TC²-GDAE-MCRS) that incorporates criterion-wise deep autoencoders, graph neural networks, contrastive self-supervised learning, temporal modeling of user preferences, and reinforcement learning in a single framework. The proposed model is evaluated using the Yahoo! Movies and TripAdvisor benchmark datasets with rating prediction and top-K ranking metrics. The results show that the proposed system reduces the RMSE from 0.912 to 0.824, MAE from 0.784 to 0.701, and NDCG@10 from 0.736 to 0.812, significantly outperforming the existing state-of-the-art models.

Keywords

Multi-Criteria Recommender Systems, Deep Autoencoders, Graph Neural Networks, Contrastive Learning, Temporal Modeling, TC²-GDAE-MCRS.

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