Temporal Proximity induces Attributes Similarity

TL;DR

This paper introduces a novel temporal proximity filtering method that leverages user consumption patterns to improve item matching in recommender systems by inducing attribute similarity based on temporal proximity.

Contribution

It presents a new temporal proximity filtering approach that models user preferences without external knowledge, enhancing item similarity learning in recommender systems.

Findings

Proximity preferences are demonstrated to exist.

A new similarity metric based on temporal proximity is introduced.

The model improves item matching by capturing user tastes through temporal patterns.

Abstract

Users consume their favorite content in temporal proximity of consumption bundles according to their preferences and tastes. Thus, the underlying attributes of items implicitly match user preferences, however, current recommender systems largely ignore this fundamental driver in identifying matching items. In this work, we introduce a novel temporal proximity filtering method to enable items-matching. First, we demonstrate that proximity preferences exist. Second, we present an induced similarity metric in temporal proximity driven by user tastes and third, we show that this induced similarity can be used to learn items pairwise similarity in attribute space. The proposed model does not rely on any knowledge outside users' consumption bundles and provide a novel way to devise user preferences and tastes driven novel items recommender.