A comparative analysis of recommender system approaches

• Main Authors: Alabastro, Paolo Eduardo Carmelo C., Ang, Mary Jeanne C., De Guzman, Rigor L., Muhi, Marijo S.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Subjects: Recommender systems (Information filtering); recommender systems automate; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/etd_bachelors/11312
• Institution: De La Salle University

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.