Context-Aware Recommendations Based on Deep Learning Frameworks

Moshe Unger, Alexander Tuzhilin, Amit Livne

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

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.

Original languageEnglish
Article number3386243
JournalACM Transactions on Management Information Systems
Volume11
Issue number2
DOIs
StatePublished - 1 Jul 2020

Keywords

  • Context
  • context-aware recommendation
  • deep learning
  • latent
  • neural networks

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science (all)

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