An efficient hybrid recommendation model based on collaborative filtering recommender systems

Abstract In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users’ previous information about items such as books, music, movies, ideas, and so on. Memory‐based models are...

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Autores principales:	Mohammed Fadhel Aljunid, Manjaiah Doddaghatta Huchaiah
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/cab112e903c24eb2bf321df20545830b
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spelling	oai:doaj.org-article:cab112e903c24eb2bf321df20545830b2021-11-17T03:12:43ZAn efficient hybrid recommendation model based on collaborative filtering recommender systems2468-232210.1049/cit2.12048https://doaj.org/article/cab112e903c24eb2bf321df20545830b2021-12-01T00:00:00Zhttps://doi.org/10.1049/cit2.12048https://doaj.org/toc/2468-2322Abstract In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users’ previous information about items such as books, music, movies, ideas, and so on. Memory‐based models are generally referred to as similarity‐based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory‐based approach includes user‐based CF (UCF) and item‐based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user‐item rating matrix. However, consequent to the ingrained sparsity of the user‐item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user‐item rating matrix. Detailed experimentation on two different real‐world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.Mohammed Fadhel AljunidManjaiah Doddaghatta HuchaiahWileyarticleComputational linguistics. Natural language processingP98-98.5Computer softwareQA76.75-76.765ENCAAI Transactions on Intelligence Technology, Vol 6, Iss 4, Pp 480-492 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765
spellingShingle	Computational linguistics. Natural language processing P98-98.5 Computer software QA76.75-76.765 Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah An efficient hybrid recommendation model based on collaborative filtering recommender systems
description	Abstract In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users’ previous information about items such as books, music, movies, ideas, and so on. Memory‐based models are generally referred to as similarity‐based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory‐based approach includes user‐based CF (UCF) and item‐based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user‐item rating matrix. However, consequent to the ingrained sparsity of the user‐item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user‐item rating matrix. Detailed experimentation on two different real‐world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.
format	article
author	Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah
author_facet	Mohammed Fadhel Aljunid Manjaiah Doddaghatta Huchaiah
author_sort	Mohammed Fadhel Aljunid
title	An efficient hybrid recommendation model based on collaborative filtering recommender systems
title_short	An efficient hybrid recommendation model based on collaborative filtering recommender systems
title_full	An efficient hybrid recommendation model based on collaborative filtering recommender systems
title_fullStr	An efficient hybrid recommendation model based on collaborative filtering recommender systems
title_full_unstemmed	An efficient hybrid recommendation model based on collaborative filtering recommender systems
title_sort	efficient hybrid recommendation model based on collaborative filtering recommender systems
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/cab112e903c24eb2bf321df20545830b
work_keys_str_mv	AT mohammedfadhelaljunid anefficienthybridrecommendationmodelbasedoncollaborativefilteringrecommendersystems AT manjaiahdoddaghattahuchaiah anefficienthybridrecommendationmodelbasedoncollaborativefilteringrecommendersystems AT mohammedfadhelaljunid efficienthybridrecommendationmodelbasedoncollaborativefilteringrecommendersystems AT manjaiahdoddaghattahuchaiah efficienthybridrecommendationmodelbasedoncollaborativefilteringrecommendersystems
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An efficient hybrid recommendation model based on collaborative filtering recommender systems

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