Web crawling based context aware recommender system using optimized deep recurrent neural network

Abstract Recommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the i...

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Autores principales:	Venugopal Boppana, P. Sandhya
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Context aware recommendation Web crawling User preference vector Similarity measure Deep recurrent neural network Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fc24f21a3344478b9a1899e156022eb9
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spelling	oai:doaj.org-article:fc24f21a3344478b9a1899e156022eb92021-11-21T12:02:21ZWeb crawling based context aware recommender system using optimized deep recurrent neural network10.1186/s40537-021-00534-72196-1115https://doaj.org/article/fc24f21a3344478b9a1899e156022eb92021-11-01T00:00:00Zhttps://doi.org/10.1186/s40537-021-00534-7https://doaj.org/toc/2196-1115Abstract Recommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.Venugopal BoppanaP. SandhyaSpringerOpenarticleContext aware recommendationWeb crawlingUser preference vectorSimilarity measureDeep recurrent neural networkComputer engineering. Computer hardwareTK7885-7895Information technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJournal of Big Data, Vol 8, Iss 1, Pp 1-24 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Context aware recommendation Web crawling User preference vector Similarity measure Deep recurrent neural network Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Context aware recommendation Web crawling User preference vector Similarity measure Deep recurrent neural network Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Venugopal Boppana P. Sandhya Web crawling based context aware recommender system using optimized deep recurrent neural network
description	Abstract Recommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.
format	article
author	Venugopal Boppana P. Sandhya
author_facet	Venugopal Boppana P. Sandhya
author_sort	Venugopal Boppana
title	Web crawling based context aware recommender system using optimized deep recurrent neural network
title_short	Web crawling based context aware recommender system using optimized deep recurrent neural network
title_full	Web crawling based context aware recommender system using optimized deep recurrent neural network
title_fullStr	Web crawling based context aware recommender system using optimized deep recurrent neural network
title_full_unstemmed	Web crawling based context aware recommender system using optimized deep recurrent neural network
title_sort	web crawling based context aware recommender system using optimized deep recurrent neural network
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/fc24f21a3344478b9a1899e156022eb9
work_keys_str_mv	AT venugopalboppana webcrawlingbasedcontextawarerecommendersystemusingoptimizeddeeprecurrentneuralnetwork AT psandhya webcrawlingbasedcontextawarerecommendersystemusingoptimizeddeeprecurrentneuralnetwork
_version_	1718419341431537664

Web crawling based context aware recommender system using optimized deep recurrent neural network

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