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Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization

E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to...

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Autores principales: Kutlimuratov, Alpamis, Abdusalomov, Akmalbek Bobomirzaevich, Oteniyazov, Rashid, Mirzakhalilov, Sanjar, Whangbo, Taeg Keun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654534/
https://www.ncbi.nlm.nih.gov/pubmed/36365921
http://dx.doi.org/10.3390/s22218224
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author Kutlimuratov, Alpamis
Abdusalomov, Akmalbek Bobomirzaevich
Oteniyazov, Rashid
Mirzakhalilov, Sanjar
Whangbo, Taeg Keun
author_facet Kutlimuratov, Alpamis
Abdusalomov, Akmalbek Bobomirzaevich
Oteniyazov, Rashid
Mirzakhalilov, Sanjar
Whangbo, Taeg Keun
author_sort Kutlimuratov, Alpamis
collection PubMed
description E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process.
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spelling pubmed-96545342022-11-15 Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization Kutlimuratov, Alpamis Abdusalomov, Akmalbek Bobomirzaevich Oteniyazov, Rashid Mirzakhalilov, Sanjar Whangbo, Taeg Keun Sensors (Basel) Article E-commerce systems experience poor quality of performance when the number of records in the customer database increases due to the gradual growth of customers and products. Applying implicit hidden features into the recommender system (RS) plays an important role in enhancing its performance due to the original dataset’s sparseness. In particular, we can comprehend the relationship between products and customers by analyzing the hierarchically expressed hidden implicit features of them. Furthermore, the effectiveness of rating prediction and system customization increases when the customer-added tag information is combined with hierarchically structured hidden implicit features. For these reasons, we concentrate on early grouping of comparable customers using the clustering technique as a first step, and then, we further enhance the efficacy of recommendations by obtaining implicit hidden features and combining them via customer’s tag information, which regularizes the deep-factorization procedure. The idea behind the proposed method was to cluster customers early via a customer rating matrix and deeply factorize a basic WNMF (weighted nonnegative matrix factorization) model to generate customers preference’s hierarchically structured hidden implicit features and product characteristics in each cluster, which reveals a deep relationship between them and regularizes the prediction procedure via an auxiliary parameter (tag information). The testimonies and empirical findings supported the viability of the proposed approach. Especially, MAE of the rating prediction was 0.8011 with 60% training dataset size, while the error rate was equal to 0.7965 with 80% training dataset size. Moreover, MAE rates were 0.8781 and 0.9046 in new 50 and 100 customer cold-start scenarios, respectively. The proposed model outperformed other baseline models that independently employed the major properties of customers, products, or tags in the prediction process. MDPI 2022-10-27 /pmc/articles/PMC9654534/ /pubmed/36365921 http://dx.doi.org/10.3390/s22218224 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kutlimuratov, Alpamis
Abdusalomov, Akmalbek Bobomirzaevich
Oteniyazov, Rashid
Mirzakhalilov, Sanjar
Whangbo, Taeg Keun
Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title_full Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title_fullStr Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title_full_unstemmed Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title_short Modeling and Applying Implicit Dormant Features for Recommendation via Clustering and Deep Factorization
title_sort modeling and applying implicit dormant features for recommendation via clustering and deep factorization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9654534/
https://www.ncbi.nlm.nih.gov/pubmed/36365921
http://dx.doi.org/10.3390/s22218224
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