Latent semantic indexing collaborative filtering recommendation system

The recent increase in the amount of information available online pushed the traditional query-based search methods to the limit. The information retrieval (IR) community made a counterproposal stating that building a personalized web surfing experience to the user. The aim of this research was to d...

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Main Author:	Kim, Taesu
Format:	text
Language:	English
Published:	Animo Repository 2011
Subjects:	Computer Sciences
Online Access:	https://animorepository.dlsu.edu.ph/etd_bachelors/2642
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Institution:	De La Salle University
Language:	English

id	oai:animorepository.dlsu.edu.ph:etd_bachelors-3642
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:etd_bachelors-36422021-06-15T05:57:57Z Latent semantic indexing collaborative filtering recommendation system Kim, Taesu The recent increase in the amount of information available online pushed the traditional query-based search methods to the limit. The information retrieval (IR) community made a counterproposal stating that building a personalized web surfing experience to the user. The aim of this research was to design a recommendation system that uses Tversky commonality model with LSI algorithm to solve the issues that the traditional collaborative filtering based recommender systems pose: sparsity and scalability. With the help of the commonality and similarity measurement, The LSI algorithm with commonality and similarity performed better than the traditional LSI-based recommendation algorithm. 2011-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/2642 Bachelor's Theses English Animo Repository Computer Sciences
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
language	English
topic	Computer Sciences
spellingShingle	Computer Sciences Kim, Taesu Latent semantic indexing collaborative filtering recommendation system
description	The recent increase in the amount of information available online pushed the traditional query-based search methods to the limit. The information retrieval (IR) community made a counterproposal stating that building a personalized web surfing experience to the user. The aim of this research was to design a recommendation system that uses Tversky commonality model with LSI algorithm to solve the issues that the traditional collaborative filtering based recommender systems pose: sparsity and scalability. With the help of the commonality and similarity measurement, The LSI algorithm with commonality and similarity performed better than the traditional LSI-based recommendation algorithm.
format	text
author	Kim, Taesu
author_facet	Kim, Taesu
author_sort	Kim, Taesu
title	Latent semantic indexing collaborative filtering recommendation system
title_short	Latent semantic indexing collaborative filtering recommendation system
title_full	Latent semantic indexing collaborative filtering recommendation system
title_fullStr	Latent semantic indexing collaborative filtering recommendation system
title_full_unstemmed	Latent semantic indexing collaborative filtering recommendation system
title_sort	latent semantic indexing collaborative filtering recommendation system
publisher	Animo Repository
publishDate	2011
url	https://animorepository.dlsu.edu.ph/etd_bachelors/2642
_version_	1712575936757497856

Latent semantic indexing collaborative filtering recommendation system

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