MRIF: Multi-resolution Interest Fusion for Recommendation

TL;DR

This paper introduces MRIF, a multi-resolution interest fusion model that effectively captures and combines users' dynamic preferences across different temporal ranges, improving recommendation accuracy.

Contribution

The paper proposes a novel multi-resolution interest fusion model that considers both long-term and short-term user interests simultaneously, addressing limitations of existing methods.

Findings

MRIF outperforms state-of-the-art recommendation methods.

The model effectively captures dynamic interest changes over time.

Fusion of multi-resolution interests improves prediction accuracy.

Abstract

The main task of personalized recommendation is capturing users' interests based on their historical behaviors. Most of recent advances in recommender systems mainly focus on modeling users' preferences accurately using deep learning based approaches. There are two important properties of users' interests, one is that users' interests are dynamic and evolve over time, the other is that users' interests have different resolutions, or temporal-ranges to be precise, such as long-term and short-term preferences. Existing approaches either use Recurrent Neural Networks (RNNs) to address the drifts in users' interests without considering different temporal-ranges, or design two different networks to model long-term and short-term preferences separately. This paper presents a multi-resolution interest fusion model (MRIF) that takes both properties of users' interests into consideration. The proposed model is capable to capture the dynamic changes in users' interests at different temporal-ranges, and provides an effective way to combine a group of multi-resolution user interests to make predictions. Experiments show that our method outperforms state-of-the-art recommendation methods consistently.