Personalized Thread Recommendation on Thai Internet Forum

Abstract

The rise of user-generated content on the Internet today has led to the problem of data overload. Therefore, recommender systems have been introduced in various social platforms to automatically serve interesting content to users. Pantip.com is the most popular Thai Internet forum where people can discuss ideas, tips, and news on a variety of topics. Although Pantip has many recommendation services, these are not specific for individual users. In this paper, we proposed a personalized thread recommender system that is applicable to the Pantip site. The approach finds out appropriate threads for each user based on three aspects: user interests, thread trends, and thread freshness along with the analysis in changing of user behavior over time. We conducted experiments on the Pantip clickstream dataset and evaluated the performance by real users. Experimental results show that the proposed approach recommends threads that are significantly more satisfying for users than the baseline approaches.