Exploring Statistical Language Models for Recommender Systems

Abstract

Even though there exist multiple approaches to build recommendation algorithms, algebraic techniques based on vector and matrix representations are predominant in the field. Notwithstanding the fact that these algebraic Collaborative Filtering methods have been demonstrated to be very effective in the rating prediction task, they do not generally provide good results in the top-N recommendation task. In this research, we return to the roots of recommender systems and we explore the relationship between Information Filtering and Information Retrieval. We think that probabilistic methods taken from the latter field such as statistical Language Models can be a more effective and formal way for generating personalised ranks of recommendations. We compare our improvements against several algebraic and probabilistic state-of-the-art algorithms and pave the way to future and promising research directions.