WITHDRAWN: IOT enabled food recommender with NIR system

Shabanabegum, S.K.; Anusha, P.; Seethalakshmi, E.; Shunmugam, M.S.; Vadivukkarasi, K.; Ponnusamy, Vijayakumar

doi:10.1016/j.matpr.2020.11.302

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In [ 29 ], the authors extended the previous proposed model, while also studying the usefulness of adopting the traditional recommendation approaches on IoT service recommendation. In [ 30 ], the authors proposed an IoT food recommender inside a refrigerator for healthy living. This is a system to recommend food recipes to the user with the ability to identify the raw food available in the refrigerator and ensure their safety.…”

Section: Related Workmentioning

confidence: 99%

Product Quality Assessment in the Internet of Things: A Consumer-Oriented Approach

Naem

Koudil

Imam

2022

Sensors

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This paper proposes a consumer-oriented approach to IoT product recommendations. It is designed to help new consumers choose high-quality IoT products that best meet their needs. We used hybrid techniques to implement the proposed approach. Experiments were also conducted to implement an intelligent IoT marketing system at the Rehab marketplace. The system has shown good results in its performance, usability, and user satisfaction. These results confirm the applicability and effectiveness of the approach in assessing and recommending IoT products.

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Section: Related Workmentioning

confidence: 99%

Product Quality Assessment in the Internet of Things: A Consumer-Oriented Approach

Naem

Koudil

Imam

2022

Sensors

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“…This research presents a hierarchical attention-based food recommendation approach to prioritize users' preferences. The authors of [14] developed a new model to suggest different foods to the users based on the raw foods in the refrigerator. Also, in [42], the authors proposed a novel algorithm for extracting customer food preferences from online restaurant reviews where natural language processing techniques are used to extract food names from user comments.…”

Section: Food Recommendation Systemsmentioning

confidence: 99%

“…In recent years, with the recent advancements of online food applications, many food recommendation systems have been designed to respond to accommodate user needs in seeking relevant foods according to their tastes [13][14][15]. However, there are still considerable challenges in this domain that should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement

Ahmadian

Rostami

Jalali

et al. 2022

Int J Comput Intell Syst

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Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model.

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