TY - JOUR
T1 - Context-Aware Recommendations Based on Deep Learning Frameworks
AU - Unger, Moshe
AU - Tuzhilin, Alexander
AU - Livne, Amit
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Context
KW - context-aware recommendation
KW - deep learning
KW - latent
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85090456710&partnerID=8YFLogxK
U2 - 10.1145/3386243
DO - 10.1145/3386243
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AN - SCOPUS:85090456710
SN - 2158-656X
VL - 11
JO - ACM Transactions on Management Information Systems
JF - ACM Transactions on Management Information Systems
IS - 2
M1 - 3386243
ER -