IoT Based Weather and Location Aware Recommender System

Chakraverty, Shampa; Mithal, Aseem

doi:10.1109/confluence.2018.8442454

Cited by 9 publications

(9 citation statements)

References 8 publications

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“…In [40], the CF approach was adapted to address this issue. Here the authors exploited the weather and location data that was collected by the sensors to provide effective recommendations for the residents of that geographical region.…”

Section: Collaborative Filtering Approachmentioning

confidence: 99%

“…The system then monitors and analyses the smart devices' traffic to predict service recommendations that match their preferences. Another application [40] exploited IoT data, such as weather data, to provide several recommendations that are based on the weather. It uses sensory data to collect weather observation sequences and analyses them.…”

Section: Personal Recommender System Applicationsmentioning

confidence: 99%

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A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan

Yao

Wang

et al. 2021

The Computer Journal

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Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

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Section: Collaborative Filtering Approachmentioning

confidence: 99%

Section: Personal Recommender System Applicationsmentioning

confidence: 99%

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan

Yao

Wang

et al. 2021

The Computer Journal

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“…Although the collaborative filtering approach has been adapted in numerous studies [16][17][18][19], there are potential shortcomings that make it inefficient for RSIoT particularly, in terms of large amount of data, cold start problem, and data sparsity. In content-based, instead of relying on ratings, it recommends items that are similar to the items previously targeted by the user [20].…”

Section: Related Workmentioning

confidence: 99%

Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

et al. 2021

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Recommendation systems are crucial in the provision of services to the elderly with Alzheimer’s disease in IoT-based smart home environments. In this work, a Reminder Care System (RCS) is presented to help Alzheimer patients live in and operate their homes safely and independently. A contextual bandit approach is utilized in the formulation of the proposed recommendation system to tackle dynamicity in human activities and to construct accurate recommendations that meet user needs without their feedback. The system was evaluated based on three public datasets using a cumulative reward as a metric. Our experimental results demonstrate the feasibility and effectiveness of the proposed Reminder Care System for real-world IoT-based smart home applications.

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“…It is considered that the main goal of RSs is to know user preferences, as this enables them to conduct accurate recommendations. In [21], the CF approach was adapted to address this issue. Here, the authors exploited the weather and location data that was collected by the sensors to provide effective recommendations for the residents of that geographical region.…”

Section: Collaborative Filtering Approachmentioning

confidence: 99%

“…The system then monitors and analyses the smart devices' traffic to predict services recommendations which match their preferences. Another application [21] exploited IoT data, such as weather data, to provide several recommendations that are based on the weather. It uses sensory data to collect weather observation sequences and analyses them.…”

Section: Personal Recommender System Applicationsmentioning

confidence: 99%

Recommender Systems for the Internet of Things: A Survey

Altulyan,

Yao,

Wang

et al. 2020

Preprint

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Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.

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IoT Based Weather and Location Aware Recommender System

Cited by 9 publications

References 8 publications

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Context-Induced Activity Monitoring for On-Demand Things-of-Interest Recommendation in an Ambient Intelligent Environment

Recommender Systems for the Internet of Things: A Survey

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