Consistence beats causality in recommender systems

TL;DR

This paper argues that in recommender systems, user interests are often stable and consistent over time, suggesting that focusing on consistency rather than causality can improve recommendation accuracy.

Contribution

The paper introduces a novel consistency-based recommendation algorithm that outperforms existing causality-driven methods across multiple real-world datasets.

Findings

The proposed algorithm achieves higher accuracy than state-of-the-art methods.

Consistency-based approach is effective across diverse datasets.

Temporal order may not imply causality in user preferences.

Abstract

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}.