PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions

TL;DR

PARSRec introduces an explainable, personalized attention-based recurrent model that leverages session partial actions to improve sequential recommendations by capturing individual user behaviors, achieving significant performance gains.

Contribution

The paper presents a novel architecture combining explainability and personalization in sequential recommendation using session partial actions.

Findings

Achieves up to 27.9% performance improvement over state-of-the-art methods.

Learns interpretable personalized user behaviors.

Effectively combines common patterns with individual behaviors.

Abstract

The emerging meta- and multi-verse landscape is yet another step towards the more prevalent use of already ubiquitous online markets. In such markets, recommender systems play critical roles by offering items of interest to the users, thereby narrowing down a vast search space that comprises hundreds of thousands of products. Recommender systems are usually designed to learn common user behaviors and rely on them for inference. This approach, while effective, is oblivious to subtle idiosyncrasies that differentiate humans from each other. Focusing on this observation, we propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Simulations under a controlled environment show that our proposed model learns interpretable personalized user behaviors. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement compared to the state-of-the-art.