Journal
KNOWLEDGE-BASED SYSTEMS
Volume 215, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2021.106740
Keywords
Context-Aware Recommender Systems; Mobile computing; Context-aware computing; Personalization; Information management
Categories
Funding
- AEI/FEDER, UE [TIN2016-78011-C4-3-R]
- Government of Aragon [T64_20R]
- Connecting Europe Facility of the European Union [2017-EU-IA-0167]
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In the field of artificial intelligence, recommender systems, particularly in the area of machine learning, have been highly researched. These systems aim to combat information overload from the increasing volume of data in the Big Data era by offering personalized suggestions. With the rise of mobile computing, these systems have evolved to include the dynamic context of mobile users for more relevant recommendations.
In the Artificial Intelligence (AI) field, and particularly within the area of Machine Learning (ML), recommender systems have attracted significant research attention. These systems attempt to alleviate the increasing information overload that users can experience in the current Big Data era, by providing personalized recommendations of items that they may find relevant. Besides, given the importance of mobile computing, these systems have evolved to consider also the dynamic context of the mobile users (location, time, weather conditions, etc.) to offer them more appropriate suggestions and information while on the move. In this paper, we provide an extensive survey of recent advances towards intelligent mobile Context-Aware Recommender Systems (mobile CARS) from an information management perspective, with an emphasis on mobile computing and AI techniques, along with an analysis of existing research gaps and future research directions. We focus on approaches that go beyond just considering the location of the user and exploit also other context information. In this study, we have identified that deep learning approaches are promising artificial intelligence models for mobile CARS. Additionally, in a near future, we expect a higher prominence of push-based recommendation solutions where at least part of the recommendation engine could be executed in the mobile devices, which could share data and tasks in a distributed way. (C) 2021 Elsevier B.V. All rights reserved.
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