A comparative analysis of recommender system approaches
Date of Publication
2009
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
Merlin Suarez
Defense Panel Member
Raymund Sison
Remedios Bulos
Abstract/Summary
Recommendations among people first consider several factors such as interests prior to the actual recommendation. Today, recommender systems automate this process. However, different recommendation system approaches vary in coverage and accuracy of recommendations especially with respect to the domain it is applied. And now, with the utilization of recommender systems into mobile devices, these variations have become more significant. This paper aims to compare four recommender system approaches namely: collaborative, content-based, collaborative with context, and content-based with context in the domain of museum guides on handheld devices. These approaches will be analyzed based on coverage and accuracy.
Abstract Format
html
Language
English
Format
Accession Number
TU19844
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
various foliations ; 28 cm.
Keywords
Recommender systems (Information filtering); recommender systems automate
Recommended Citation
Alabastro, P. C., Ang, M. C., De Guzman, R. L., & Muhi, M. S. (2009). A comparative analysis of recommender system approaches. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/11312
