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Context-Aware Recommendations Based on Deep Learning Frameworks

Context-Aware Recommendations Based on Deep Learning Frameworks In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Context-Aware Recommendations Based on Deep Learning Frameworks

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
2158-656X
eISSN
2158-6578
DOI
10.1145/3386243
Publisher site
See Article on Publisher Site

Abstract

In this article, we suggest a novel deep learning recommendation framework that incorporates contextual information into neural collaborative filtering recommendation approaches. Since context is often represented by dynamic and high-dimensional feature space in multiple applications and services, we suggest to model contextual information in various ways for multiple purposes, such as rating prediction, generating top-k recommendations, and classification of users’ feedback. Specifically, based on the suggested framework, we propose three deep context-aware recommendation models based on explicit, unstructured, and structured latent representations of contextual data derived from various contextual dimensions (e.g., time, location, user activity). Offline evaluation on three context-aware datasets confirms that our proposed deep context-aware models surpass state-of-the-art context-aware methods. We also show that utilizing structured latent contexts in the proposed deep recommendation framework achieves significantly better performance than the other context-aware models on all datasets.

Journal

ACM Transactions on Management Information Systems (TMIS)Association for Computing Machinery

Published: May 19, 2020

Keywords: Context

References