A Survey on Recommendation Systems in Thailand B. E. 2550 - 2560

Abstract

       This survey research attempts to explore research papers on recommendation systems in Thailand, published from 2007 to 2017 and review them to identify research trends in recommender systems.  Thirty four research papers are retrieved for the study through the use of keywords to search ThaiLIS database, select the domain of recommended system and eliminate irrelevant items.  The papers are classified based on filtering types, information utilization,  and classification technique.  The most common filtering method used in the recommendation systems is collaborative filtering.  The information on films and electronic commerce (e-commerce) is heavily used in engaging tasks and conducting researches.  The most popular classification technique is Naïve Bayesian classification.