A New Hybrid-Enhanced Recommender System for Mitigating Cold Start Issues

Idrissi, Nouhaila; Zellou, Ahmed; Hourrane, Oumaima; Bakkoury, Zohra; Benlahmar, El Habib

doi:10.1145/3373744.3373746

Cited by 3 publications

(1 citation statement)

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“…Trust-based methods [6,7] substitute unavailable evaluations with imputed entries by exploring the trust network information of users and items. Such trust-aware techniques can enhance the performance of Non-negative Matrix Factorization (NMF), especially for cold-start new users who need to assess more items [7,13,14]. For an efficient Top-K recommendation, some models consider the unobserved entries in the rating matrix as negative preferences [10,11], while they are ignored in prediction-based approaches.…”

Section: Related Workmentioning

confidence: 99%

“Guess Why I didn't rate it”: A New Preference-based Model for Enhanced TopK Recommendation

2023

IJIES

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In existing matrix factorization (MF)-based recommender systems, the user-item interaction matrix is factorized linearly into two low-ranked feature matrices to generate predictions or provide the users with personalized rankings of items. However, when MF methods are directly applied to sparse rating matrices, they cannot cope with the inherent structure of real-world user and item latent features. The efficiency of MF-based systems crucially depends on their capability to address sparsity issues. To this end, we propose, in this paper, a novel preference-based data imputation approach for effective MF-based Top-K recommendation. We apply MF on an imputed and denser rating matrix with only interesting items to users. We obtain these items by inferring the prior preferences of users, considering some biases that may impact their choices for items they interact with, and leveraging a powerful latent and non-linear feature extraction using a deep generative model. Experimental results on two real-world sparse datasets reveal that the proposed model significantly enhances the performance of Top-K recommendations and outperforms baselines with an average improvement margin of 6.45% and 3.91% in hit rate (HR) and normalized discounted cumulative gain (NDCG) evaluation metrics, respectively, averaging on all employed datasets.

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