Mining personality traits from social messages for game recommender systems

摘要

Recently, recommender systems for various types of resource received lots of attention due to the need for finding interesting resources from gigantic body such as World Wide Web or social network services. An emerging branch of recommender systems tried to recommend resources to users according to their personality traits and received promising results. In this work, we proposed an approach on recommending computer games to players according to their identified personality traits. We first applied text mining processes on some textual contents related to the players to identify their personality traits using the Five Factor Model. The same personality recognition process was also applied on contents related to games. The games with similar personality traits to the players’ were then recommended to the players. We performed experiments on 63 players and 2050 games with data collected from Steam and obtained satisfying result.