Title

Latent semantic indexing collaborative filtering recommendation system

Author

Taesu. Kim

Date of Publication

2011

Document Type

Thesis

Degree Name

Bachelor of Science in Computer Science

College

College of Computer Studies

Department/Unit

Computer Science

Thesis Adviser

Abe, Lesley

Defense Panel Chair

Ong, Ethel

Defense Panel Member

Fontanilla, Gian
Lim-Cheng, Nathalie Rose

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’s 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

Format

Print

Accession Number

TU18583

Shelf Location

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

1v. various foliations ; illustrations ; 28 cm.

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