A Context-aware Recommendation System using smartphone sensors

Zou, Xueyang; Gonzales, Mariel; Saeedi, Sara

doi:10.1109/iemcon.2016.7746307

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Another model proposed in [54] uses context-aware and personalized computations to filter user query situations. Their model achieves recommendation by utilizing user context information like preferred transportation mode and location, item's context information like restaurant rating and types, and personalized preference information like individuals' past behaviour.…”

Section: Recommendation Systems In Smart Transportationmentioning

confidence: 99%

Detection of In-car Driver Distraction Activities With Recommendations

Aljasim

2024

Preprint

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The increasing number of car accidents is a significant issue in current transpiration systems. th According to the world health organization (WHO), road accidents are the 8 highest top reason of death around the world. More than 80% of road accidents are caused by distracted driving, such as using a mobile phone, talking to passengers, and smoking. A lot of efforts have been made to tackle the problem of driver distraction; however, no optimal solution is provided. A practical approach to solving this problem is implementing quantitative measures for driver activities and designing a classification system that detects distracting actions. In this project, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize the level of distractions and increase the level of in-care awareness for improved safety.

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Section: Recommendation Systems In Smart Transportationmentioning

confidence: 99%

Detection of In-car Driver Distraction Activities With Recommendations

Aljasim

2024

Preprint

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“…The authors of [50] suggested a context-aware web services recommendation for modelling impact on the user's expectations on user location updates and location similarity mining based on the user location in the smart city context. The authors of [142][143][144][145][146] proposed a context-aware recommendation system using smartphone sensors integrated with smart city applications and e-tourism, another recommendation system based on tourist context [147,148]. The authors of [49,73,149,150] proposed a system of travel recommendations that mines appropriate locations, context, user preferences [151][152][153] users' reviews [154], sentiments analysis [1], and users' physical and psychological functionality levels [155].…”

Section: Traveling and Poimentioning

confidence: 99%

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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Research on Context-Aware Recommendation Algorithm Based on RFID Application

Zhao¹

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A Context-aware Recommendation System using smartphone sensors

Cited by 4 publications

References 8 publications

Detection of In-car Driver Distraction Activities With Recommendations

Detection of In-car Driver Distraction Activities With Recommendations

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

Research on Context-Aware Recommendation Algorithm Based on RFID Application

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