Personality in Recommender Systems
Li Chen · 2015
The personality-based recommender systems (RS) has emerged as a new type of RS in recent years, given that personality contains valuable information enabling systems to better understand users' preferences [7]. This presentation first gives an overview of the state-of-the-art in this area, including the approaches developed for enhancing collaborative filtering (CF) by computing users' or items' personality similarity [1,4,5,8], as well as the one that incorporates personality into matrix factorization to predict items that users are able to rate for active learning [3].