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KI - Künstliche Intelligenz
Erschienen in: KI - Künstliche Intelligenz 4/2018

27.09.2018 | Technical Contribution

Explanations for Temporal Recommendations

verfasst von: Homanga Bharadhwaj, Shruti Joshi

Erschienen in: KI - Künstliche Intelligenz | Ausgabe 4/2018

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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.
Vorheriger Artikel Defining Explainable AI for Requirements Analysis
Nächster Artikel AI and Robotics for the Human Brain Project II

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Metadaten
Titel
Explanations for Temporal Recommendations
verfasst von
Homanga Bharadhwaj
Shruti Joshi
Publikationsdatum
27.09.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
KI - Künstliche Intelligenz / Ausgabe 4/2018
Print ISSN: 0933-1875
Elektronische ISSN: 1610-1987
DOI
https://doi.org/10.1007/s13218-018-0560-x

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