Consistence beats causality in recommender systems
This work addresses the challenge of improving recommendation accuracy for users in information-rich environments, presenting a novel approach that is not incremental.
The paper tackles the problem of recommender systems by arguing that user interests are stable and temporal order does not imply causality, proposing a consistency-based algorithm that outperforms state-of-the-art methods on datasets like Netflix, MovieLens, Amazon, and Rate Your Music.
The explosive growth of information challenges people's capability in finding out items fitting to their own interests. Recommender systems provide an efficient solution by automatically push possibly relevant items to users according to their past preferences. Recommendation algorithms usually embody the causality from what having been collected to what should be recommended. In this article, we argue that in many cases, a user's interests are stable, and thus the previous and future preferences are highly consistent. The temporal order of collections then does not necessarily imply a causality relationship. We further propose a consistence-based algorithm that outperforms the state-of-the-art recommendation algorithms in disparate real data sets, including \textit{Netflix}, \textit{MovieLens}, \textit{Amazon} and \textit{Rate Your Music}.