Location based context aware recommender system through user defined rules

Sharma, Silky; Kaur, Davinder

doi:10.1109/ccaa.2015.7148384

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…With the inception of the formal term Recommender system in the 1990s, two-dimensional RS were the only approaches in predicting users' interest until recently, researchers aimed at developing systems with the ability to recommend items to users in certain circumstances considering contextual information to provide context-aware recommendation system . The authors in [8] reported that the concept of context-aware was first introduced by the work of [9] and has significantly evolved since then. Context-aware recommender systems (CARS) extend the traditional formulation of a recommender system problem by incorporating the third dimension to the user-item interactions.…”

Section: Methodsmentioning

confidence: 99%

“…Point of interest (POI) [8], [20], [21], [22], [23], [24], [21], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [9], [37] E-commerce [38], [39], [40], [34], [41], [21], [22], [24], [42], [43], [3] Travel and tourism [24], [44], [45], [46], [47], [48], [6], [49] Entertainment [48], [50], [31], [24] Others [51], [18], [8], [52], [53], [6], [54], [49]…”

Section: Application Domain Papersmentioning

confidence: 99%

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Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Haruna¹,

Musa²,

Yunusa³

et al. 2022

SITech

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Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.

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Section: Methodsmentioning

confidence: 99%

Section: Application Domain Papersmentioning

confidence: 99%

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Haruna¹,

Musa²,

Yunusa³

et al. 2022

SITech

View full text Add to dashboard Cite

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RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

Sharma

Aujla

Bajaj

2019

Int J Communication

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The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future. KEYWORDScollaborative filtering-based recommender systems, content-based recommender systems, context-aware recommender system, health care recommender system, Healthcare 4.0, human-centric systems INTRODUCTIONThe increased demand of the health statistics and the variations in looking for information tactics can be pragmatic around the globe. 1 As per the latest study, 81% of the adults in the United States utilize the Internet services, and out of them, 59%

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Collaborating Filtering Community Image Recommendation System Based on Scene

Zhu

Xiong

et al. 2017

ITM Web Conf.

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Abstract-With the advancement of smart city, the development of intelligent mobile terminal and wireless network, the traditional text information service no longer meet the needs of the community residents, community image service appeared as a new media service. "There are pictures of the truth" has become a community residents to understand and master the new dynamic community, image information service has become a new information service. However, there are two major problems in image information service. Firstly, the underlying eigenvalues extracted by current image feature extraction techniques are difficult for users to understand, and there is a semantic gap between the image content itself and the user's understanding; secondly, in community life of the image data increasing quickly, it is difficult to find their own interested image data. Aiming at the two problems, this paper proposes a unified image semantic scene model to express the image content. On this basis, a collaborative filtering recommendation model of fusion scene semantics is proposed. In the recommendation model, a comprehensiveness and accuracy user interest model is proposed to improve the recommendation quality. The results of the present study have achieved good results in the pilot cities of Wenzhou and Yan'an, and it is applied normally.

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Location based context aware recommender system through user defined rules

Cited by 5 publications

References 17 publications

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

RETRACTED: Evolution from ancient medication to human‐centered Healthcare 4.0: A review on health care recommender systems

Collaborating Filtering Community Image Recommendation System Based on Scene

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