A Survey of Context-Aware Mobile Recommendations

Liu, Qi; Ma, Haiping; Chen, Enhong; Xiong, Hui

doi:10.1142/s0219622013500077

Cited by 88 publications

(44 citation statements)

References 126 publications

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“…[3][4][5][6][7] From an application perspective, Verbert et al 8 specifically presented a survey of context-aware recommendation systems that have been established for technology-enhanced learning. More comprehensively, Liu et al 9 surveyed the existing studies according to the entire spectrum of context-aware mobile recommendation systems and their specifications, seeking to predict and anticipate the needs of users and respond to their behavior. Makris et al 10 studied context-awareness with regard to mobile and wireless networking and provided a precise taxonomy scheme for those areas.…”

Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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Movement of an entity is greatly affected by its internal and external contexts. Such consequential influence has created new paradigms for context-aware movement data mining and analysis. The significance of incorporating contextual information and movement data is becoming quite evident because of the growing interest in context-aware movement analysis. Despite such importance, there is limited consensus among researchers on the definition of context and context-aware system design in movement studies. Therefore, this paper comprehensively reviews current concepts of context and provides a definition and a taxonomy for context in movement analysis. The paper proceeds by providing a definition of context-aware systems in the movement area after a complete review and comparison of the present definitions present in the literature. Inspired by related works, the paper further suggests a holistic three-layer design framework tailored to context-aware systems in movement studies to examine in greater depth the techniques applied during the design stages. The paper outlines the challenges and emergent issues in future research directions in context-aware movement analysis. The present study is an attempt to bridge the gap between solely using context and developing context-aware systems, thus paving the way for further research in movement applications. © 2017 Wiley Periodicals, Inc. How to cite this article:WIREs Data Mining Knowl Discov 2018Discov , 8:e1233. doi: 10.1002Discov /widm.1233 INTRODUCTIONM ovement is intrinsically a continuous phenomenon and is normally recorded as discrete snapshots at different temporal resolutions. The three fundamental sets pertinent to movement are space S (i.e., set of locations/places), time T (i.e., set of instants/intervals), and object O (i.e., set of entities). 1 In this paper, by movement we mean the change in the spatial location of one or more entities (e.g., people, animals, and vehicles) over time, not a change in entities' geometries (e.g., an individual's body parts). Such changes can be represented as a function μ: T ! S, which maps any time to a location in space. The purpose of analyzing movement data in the majority of research fields (e.g., geography, ecology, sociology, and transportation) has focused on the development of insights into the behavior of moving entities. However, the movement behavior of entities is generally associated with their internal states and external factors. 2 In other words, understanding entities' movement greatly depends on recognizing the characteristics attributed to the entity itself (e.g., the entities' purposes and intentions for movement, the entities' perceptions of space) as well as the discovery of geographical circumstances during the movement (e.g., weather conditions and traffic). We, respectively, term these influential variables internal and external contexts in this paper. of 19Because of the progress in sensing, imaging, tracking, and navigation technologies and infrastructures in recent years, unprecedented amount...

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Section: Introductionmentioning

confidence: 99%

Context‐aware movement analytics: implications, taxonomy, and design framework

Sharif

Alesheikh

2017

WIREs Data Min & Knowl

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“…Colombo-Mendoza et al (2015) proposed a context-aware, knowledge-based, mobile recommendation system for movie show times. Privacy is indeed an essential part of mobile recommender systems (del Carmen Rodríguez-Hernández & Ilarri, 2016;Liu et al, 2013). However, there is a gap between recommender systems and mobile computing., For example most of the related work is about the protection of personal user data, such as the ratings.…”

Section: Related Workmentioning

confidence: 99%

Privacy-preserving recommendations in context-aware mobile environments

Polatidis

Georgiadis

Pimenidis

et al. 2017

ICS

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Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

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“…However, there are also some limitations and key issues that should be taken care of when conducting context-aware mobile travel tour recommendation, and these limitations and issues lead to the future research directions (Liu et al 2013b;Gavalas et al 2014):…”

Section: Future Directionsmentioning

confidence: 99%