Explanations for Temporal Recommendations

TL;DR

This paper introduces a framework for explainable deep learning-based temporal recommendation systems, combining LSTM models with neighborhood explanations to improve interpretability without sacrificing accuracy.

Contribution

It presents a novel approach that integrates explanation generation into deep temporal recommendation models, specifically using LSTM and neighborhood schemes.

Findings

Effective explanations generated for recommendations

Joint optimization improves accuracy and interpretability

Demonstrated on Netflix dataset with positive results

Abstract

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI. Deep learning (DL) techniques for recommendation systems (RS) provide powerful latent-feature models for effective recommendation but suffer from the major drawback of being non-interpretable. In this paper we describe a framework for explainable temporal recommendations in a DL model. We consider an LSTM based Recurrent Neural Network (RNN) architecture for recommendation and a neighbourhood-based scheme for generating explanations in the model. We demonstrate the effectiveness of our approach through experiments on the Netflix dataset by jointly optimizing for both prediction accuracy and explainability.