ColorCocktail: an Ontology-Based Recommender System

Yu-Hsin Chen, Ting-hsiang Huang, David Chawei Hsu, Jane Yung-jen Hsu

Cocktails are popular in a wide variety of social functions. Given the diverse selection of ingredients and preparation, there are often fitting choices that match the mood or spirit of any given moment. On the other hand, specifying the ideal drink can pose a challenge for the average person without detailed knowledge about cocktails. In this paper, a novel recommender system, “ColorCocktail ”, is proposed to help people choose cocktails in accordance with their current mood and preferences, which are specified by checking a few general characteristics. In particupar, this research explores using colors as an abstract representation of personal emotions. The ColorCocktail system performs ontological reasoning using a knowledge base containing the cocktail ontology. Combined with commonsense reasoning for affect sensing from colors, the system is able to make intelligent recommendations through an intuitive interface.

Subjects: 1. Applications

Submitted: May 17, 2006


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