Title :
Recommend My Dish: A multi-sensory food recommender
Author :
Hannah Abdool;Akash Pooransingh;Ying Li
Author_Institution :
The University of the West Indies, St. Augustine, Trinidad
Abstract :
In this paper, the model for a multi-sensory food recommender is presented, which takes into account both taste and aesthetic attributes of food. The recommender was designed using a case-based reasoning (CBR) approach, and built with the myCBR framework. The recommender was later integrated into an Android application prototype, via which potential user feedback was obtained. We conducted a preliminary user study in which all participants rated their satisfaction with the recommendations above 5 on a scale of 0 to 10. Furthermore, 72% of participants felt that by considering their aesthetic preferences in the recommendation process, the system produced better recommendations than if they were not considered.
Keywords :
"Recommender systems","Cognition","Image color analysis","Collaboration","Machine learning algorithms"
Conference_Titel :
Communications, Computers and Signal Processing (PACRIM), 2015 IEEE Pacific Rim Conference on
Electronic_ISBN :
2154-5952
DOI :
10.1109/PACRIM.2015.7334841