Latent semantic indexing collaborative filtering recommendation system
Date of Publication
2011
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Computer Science
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Lesley Abe
Defense Panel Chair
Ethel Ong
Defense Panel Member
Gian Fontanilla
Nathalie Rose Cheng-Lim
Abstract/Summary
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.
Abstract Format
html
Language
English
Format
Accession Number
TU18583
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
1v. various foliations ; illustrations ; 28 cm.
Recommended Citation
Kim, T. (2011). Latent semantic indexing collaborative filtering recommendation system. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/2642
