Color Cocktail: an Ontology-Based recommender System Yu-Hsin Chen Ting-hsiang Huang David Chawei Hsu Jane Yung-jen Hsu r94922046@ntu. edu. nw r93922024@ntu.edu. hw r94921028@ntu.edu. nw yjhsu @csie ntu. edu.n Department of Computer Science and Information Engineering National Taiwan University. Taiwan Abstract This paper presents"ColorCocktail, a cocktail recom- Cocktails are popular in a wide variety of social func- mendation system that helps people identify the ideal drink ons. Given the diverse selection of ingredients and based on a few general characteristics specified by the user. preparation, there are often fitting choices that match Besides the basic ingredients, Color Cocktail considers the the mood or spirit of any given moment. On the other mutual influence between the taste of a drink and a persons hand, specifying the ideal drink can pose a challenge for mood. In particular, this research explores using colors as the average person without detailed knowledge about an abstract representation of personal emotions reflected in cocktails. In this paper, a novel recommender system the current mood. For example, one draws some colors on Color Cocktail, is proposed to help people choose the screen as shown on the left in Figure ?? Color Cocktail cocktails in accordance with their current mood and analyzes the colors to find the cocktail, shown on the right, preferences, which are specified by checking a few gen- with the matching taste and aura. eral characteristics. In particupar, this research explores sing colors as an abstract representation of personal emotions. The Color Cocktail system performs ontolog- ical reasoning using a knowledge base containing the cocktail ontology. Combined with commonsense rea- ning for affect sensing from colors, the system is able to make intelligent recommendations through an intur- ntroduction This research explores the role of ontology and common- Figure 1: Specification of mood as colors sense knowledge in supporting natural human-computer in teraction. The task of selecting cocktails to match personal Detailed knowledge about cocktails has been represented mood and preferences is used as a motivating example as a cocktail ontology. Color Cocktail performs ontological Cocktails are popular in a wide variety of social gath- reasoning based on such a taxonomic classification in find ings, ranging from formal wedding ceremonies to casual ing the recommendations. In addition, commonsense rea- parties. A cocktail is commonly known as an iced drink soning is used to link the intuitive specification of colors of wine or distilled liquor mixed with flavoring ingredients with personal emotions in order to provide a natural interac- usually one or more of a liqueur, fruit, sauce, honey, milk tive recommendation process r cream,spices,etc. Cocktails became popular from 1920 The rest of this paper is organized as follows. We start and continuously evolve for decades. People are fascinated by surveying related work in recommender systems, human vith the variety of dazzling colors and attractive tastes. A taste perception, as well as color and emotion. We then great choice of cocktail can enhance the experience of spe- present the Color Cocktail system architecture together with cial moments le lives. However findi more detailed descriptions of the cocktail ontology, drink can be a big challenge for the average person without terface, and the reasoning component in the prototype sys- detailed knowledge about cocktails. People usually take the em. Finally, we discuss some preliminary findings from easy way out by going for known popular choices or rec- testing the Color Cocktail prototype system for cocktail rec ommendations from the bartender. wouldn 't it be nice to ommendations and suggest several important issues for fu- have an intelligent recommender system that can help a per- ture research son make the wise choice according to her current mood and preferences? Related work Copyright C 2007, American Association for Artificial Intelli To design a good cocktail recommendation system, we need gence(www.aaai.org).Allrightsreserved to explore related research in the areas of recommender sys-ColorCocktail: an Ontology-Based Recommender System Yu-Hsin Chen Ting-hsiang Huang David Chawei Hsu Jane Yung-jen Hsu r94922046@ntu.edu.tw r93922024@ntu.edu.tw r94921028@ntu.edu.tw yjhsu@csie.ntu.edu.tw Department of Computer Science and Information Engineering National Taiwan University, Taiwan Abstract Cocktails are popular in a wide variety of social func￾tions. 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 gen￾eral characteristics. In particupar, this research explores using colors as an abstract representation of personal emotions. The ColorCocktail system performs ontolog￾ical reasoning using a knowledge base containing the cocktail ontology. Combined with commonsense rea￾soning for affect sensing from colors, the system is able to make intelligent recommendations through an intu￾itive interface. Introduction This research explores the role of ontology and common￾sense knowledge in supporting natural human-computer in￾teraction. The task of selecting cocktails to match personal mood and preferences is used as a motivating example. Cocktails are popular in a wide variety of social gath￾erings, ranging from formal wedding ceremonies to casual parties. A ‘cocktail’ is commonly known as an iced drink of wine or distilled liquor mixed with flavoring ingredients, usually one or more of a liqueur, fruit, sauce, honey, milk or cream, spices, etc. Cocktails became popular from 1920 and continuously evolve for decades. People are fascinated with the variety of dazzling colors and attractive tastes. A great choice of cocktail can enhance the experience of spe￾cial moments in people lives. However, finding the right drink can be a big challenge for the average person without detailed knowledge about cocktails. People usually take the easy way out by going for known popular choices or rec￾ommendations from the bartender. Wouldn’t it be nice to have an intelligent recommender system that can help a per￾son make the wise choice according to her current mood and preferences? Copyright c 2007, American Association for Artificial Intelli￾gence (www.aaai.org). All rights reserved. This paper presents “ColorCocktail”, a cocktail recom￾mendation system that helps people identify the ideal drink based on a few general characteristics specified by the user. Besides the basic ingredients, ColorCocktail considers the mutual influence between the taste of a drink and a person’s mood. In particular, this research explores using colors as an abstract representation of personal emotions reflected in the current mood. For example, one draws some colors on the screen as shown on the left in Figure ??. ColorCocktail analyzes the colors to find the cocktail, shown on the right, with the matching taste and aura. Figure 1: Specification of mood as colors. Detailed knowledge about cocktails has been represented as a cocktail ontology. ColorCocktail performs ontological reasoning based on such a taxonomic classification in find￾ing the recommendations. In addition, commonsense rea￾soning is used to link the intuitive specification of colors with personal emotions in order to provide a natural interac￾tive recommendation process. The rest of this paper is organized as follows. We start by surveying related work in recommender systems, human taste perception, as well as color and emotion. We then present the ColorCocktail system architecture together with more detailed descriptions of the cocktail ontology, user in￾terface, and the reasoning component in the prototype sys￾tem. Finally, we discuss some preliminary findings from testing the ColorCocktail prototype system for cocktail rec￾ommendations and suggest several important issues for fu￾ture research. Related Work To design a good cocktail recommendation system, we need to explore related research in the areas of recommender sys-