Matrix Factorization and Regression-Based Approach for Multi-Criteria Recommender System

Majumder, Gouri Sankar; Dwivedi, Pragya; Kant, Vibhor

doi:10.1007/978-3-319-63673-3_13

Cited by 4 publications

(2 citation statements)

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“…Several techniques are proposed for computing similarities such as Euclidean distance [17], Mahalanobis distance [18] grey relational analysis [19,20] for enhancing the accuracy of referrals in similarity-based methods. To improve the accuracy of predictions in aggregation function-based MCCF systems, a part of the researchers try to produce more accurate criterion-based predictions [21][22][23][24][25][26]. Criterion-based predictions are generated by matrix factorization [23], fuzzy Bayesian approach [21], autoencoders [25], multi-layer neural networks [26].…”

Section: Related Workmentioning

confidence: 99%

“…To improve the accuracy of predictions in aggregation function-based MCCF systems, a part of the researchers try to produce more accurate criterion-based predictions [21][22][23][24][25][26]. Criterion-based predictions are generated by matrix factorization [23], fuzzy Bayesian approach [21], autoencoders [25], multi-layer neural networks [26]. Rest of the researchers try to enhance the accuracy of predictions by integrating more precise aggregation function [27][28][29][30][31].…”

Section: Related Workmentioning

confidence: 99%

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A new similarity-based multi-criteria recommendation algorithm based on autoencoders

2021

Turk J Elec Eng & Comp Sci

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Recommender systems provide their users an efficient way to handle with information overload problem by offering personalized suggestions. Traditional recommender systems are based on two-dimensional user-item preference matrix which constructed depending on the users' overall evaluations over items. However, they have begun to present their preferences over under various circumstances. Thus, traditional recommendation techniques fail to process multicriteria ratings during the recommendation process. Multi-criteria recommender systems are an extension of traditional recommender systems that utilize multi-criteria-based user preferences. Multi-criteria recommender systems provide more personalized and accurate predictions compared to traditional recommender systems. However, the increased amount of data dimension causes sparsity to be a major problem of such systems. Especially, the similarity-based multi-criteria recommender systems may fail to find similar neighbors to an active user due to the lack of co-rated items among users.Therefore, we propose a new similarity-based multi-criteria collaborative filtering approach based on autoencoders. In order to handle with sparsity, the proposed method extracts non-linear, low-dimensional, dense features from raw and sparse users'/items' preferences. Our experimental outcomes show that the proposed work can amortize the negative impacts of sparsity over the accuracy comparing with the state-of-the-art multi-criteria recommendation techniques.

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