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Explanations for Temporal Recommendations

Abstract

Recommendation systems (RS) 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 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 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.

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Correspondence to Homanga Bharadhwaj.

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Bharadhwaj, H., Joshi, S. Explanations for Temporal Recommendations. Künstl Intell 32, 267–272 (2018) doi:10.1007/s13218-018-0560-x

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Keywords

  • Recommendation systems
  • Explainable AI
  • Recurrent Neural Networks