Thresholding for Top-k Recommendation with Temporal Dynamics

TL;DR

This paper introduces a time-dependent bias learning method for top-k recommendation systems that adapt to temporal data shifts, improving performance in dynamic environments.

Contribution

It proposes an alternating optimization framework for learning item biases over time, addressing data sparsity and trend shifts in recommendation systems.

Findings

Bias learning consistently improves recommendation accuracy.

Method effectively handles large-scale data with many users and items.

Approach performs well both offline and online on real-world datasets.

Abstract

This work focuses on top-k recommendation in domains where underlying data distribution shifts overtime. We propose to learn a time-dependent bias for each item over whatever existing recommendation engine. Such a bias learning process alleviates data sparsity in constructing the engine, and at the same time captures recent trend shift observed in data. We present an alternating optimization framework to resolve the bias learning problem, and develop methods to handle a variety of commonly used recommendation evaluation criteria, as well as large number of items and users in practice. The proposed algorithm is examined, both offline and online, using real world data sets collected from the largest retailer worldwide. Empirical results demonstrate that the bias learning can almost always boost recommendation performance. We encourage other practitioners to adopt it as a standard component in recommender systems where temporal dynamics is a norm.