A Restricted Boltzmann Machine-based Recommender System For Alleviating Sparsity Issues

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

doi:10.1109/icssd47982.2019.9003149

Cited by 3 publications

(1 citation statement)

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“…We employ the deep generative model for missing rating prediction by learning relevant hidden features of user 𝑢 and item 𝑖. The DBN is formed by stacking multiple probabilistic building blocks called restricted boltzmann machines (RBMs) [18,19] used to apprehend one layer of latent features at a Since no unit has a connection with another unit of the same layer, the values of units in the visible or hidden layer are independent concerning the units of the same layer. Therefore, the joint probability 𝑃 Θ (𝑣, ℎ) of each visible and hidden unit can be computed as follows:…”

Section: Extraction Of User and Item Featuresmentioning

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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Section: Extraction Of User and Item Featuresmentioning

confidence: 99%