@article {10.3844/jcssp.2026.2769.2782,
article_type = {journal},
title = {Hybrid Enhanced Neighbourhood and Latent Factor Model for Cold-Start Recommendations},
author = {P, Amritha and KK, Rajkumar},
volume = {22},
number = {9},
year = {2026},
month = {Sep},
pages = {2769-2782},
doi = {10.3844/jcssp.2026.2769.2782},
url = {https://thescipub.com/abstract/jcssp.2026.2769.2782},
abstract = {Among different recommendation strategies, collaborative filtering remains a commonly utilized method for generating personalized suggestions. The traditional collaborative algorithms face performance declines due to the sparse rating of data and the item cold-start problem. To overcome these challenges, this paper introduces a novel hybrid model called HCE-KNNCF (Hybrid Cognition-Enabled K-Nearest Neighbor Collaborative Filtering). The proposed model generates predicted rating by a combination of SVD-based matrix factorization and the enhanced KNN model using a weighted hybrid approach. The cognition-based KNN ensures that only relevant neighbors contribute to the rating prediction phase and the SVD-based collaborative approach is employed to model latent user-item relationships, thereby mitigating the effects of data sparsity. Experimental evaluations on the MovieLens 100 K, MovieLens 1 M, and Book-Crossing datasets show that HCE-KNNCF achieves improved prediction accuracy compared with most traditional and hybrid benchmark models. The model achieves the best MAE and RMSE results on the MovieLens 100 K and Book-Crossing datasets, while maintaining competitive performance on MovieLens 1 M. In cold-start scenarios, HCE-KNNCF demonstrate that a small increase in MAE and RMSE, indicating that the proposed approach remains stable when interaction data are limited.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}