Version 1
: Received: 17 September 2017 / Approved: 18 September 2017 / Online: 18 September 2017 (08:54:04 CEST)
How to cite:
Saeedi, S.; Zou, X.; Gonzales, M.; Liang, S. Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application. Preprints2017, 2017090074. https://doi.org/10.20944/preprints201709.0074.v1
Saeedi, S.; Zou, X.; Gonzales, M.; Liang, S. Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application. Preprints 2017, 2017090074. https://doi.org/10.20944/preprints201709.0074.v1
Saeedi, S.; Zou, X.; Gonzales, M.; Liang, S. Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application. Preprints2017, 2017090074. https://doi.org/10.20944/preprints201709.0074.v1
APA Style
Saeedi, S., Zou, X., Gonzales, M., & Liang, S. (2017). Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application. Preprints. https://doi.org/10.20944/preprints201709.0074.v1
Chicago/Turabian Style
Saeedi, S., Mariel Gonzales and Steve Liang. 2017 "Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application" Preprints. https://doi.org/10.20944/preprints201709.0074.v1
Abstract
The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.
Keywords
recommendation system; context awareness; location based services; mobile computing, cloud-based computing
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
