time first and then the reasoner does the taxonomy classifica- Acknowledgements tion to get the suitable cocktails from the data set The col First of all, the authors would like to express their thanks to orCocktail system takes the inferred instances of this equiva- the members of the Intelligent Agents Lab at National Tai- lent class as the final recommendation, presenting the image wan University. They have many made valuable suggestions and direction about recommended cocktails for a user for the design and implementation of the prototype system We use RACER as our DL reasoner, moreover, we want to This research is sponsored in part by a grant from Intel and add common sense(?)and use common sense reasoning to the National Science Council of Taiwan (NSC94-2218-E enhance the system. Due to the natural relationship between 002-057) emotion and common sense, we think that more complicated analysis can be done and the system should give a better result. This part of work is still on going. We may leave it Preliminary Results and Future Work fter the Color Cocktail system has been preliminary work ask people try to use the recommender in a free way. All of them feel very interesting in such kind of system. One of the user said that she would like to use it when having a party next time and she did not need to think everything need by herself without good suggestion. She thought that the recommender can save time for preparing the foods and drinks. Furthermore, she would want the system to know the foods that she would cook, then gave her some suggestion on the beverages, especially the cocktails. Another user told us that he was boring to wait the system result showing u The procedure of computing took too long time. He would prefer a more fast system with the closed result. The third user said that she was not familiar with wines and cocktails so she had no idea about the accuracy of the system. But she said that this system can gave her new choice of beverage when ship and some history of cocktails from the system, when she was going to drink something She could know the she could try to make some different order. From the study above, the pros and cons of the work is shown. Recommending cocktail for people is a good idea because of the lack of knowledge about the drink itself. peo- ple need suggestions in many situation; cocktail choice is a good application. In addition, the friendliness and easiness the basic requirements. Most users do not like a fancy looked system with complicated operation. So, we are trying to make a more intuitive user interface On the other hand. Color Cocktail still need to be im- proved. The processing time is the first issue. Users do not have patience to wait, so we need to control the procedure time. Color Cocktail takes too long time to give a recom- mend. We'd better reduce the computational time. Besides the named cocktails, there are still many excel lent cocktails without names. The kind of cocktails are pro- vided occasionally. Bartenders give customers the recom- mendation by both their cocktail knowledge and their expe rience. We want to add the experience to the Color Cocktail ystem. The experience representation is formal, eed commonsense reasoning to help us to convert user Use er can interact with the system intuitively. Also, commonsense rea- soning can be used in suggestion inference We want have more feed back about the system, hence we will put the work on Web. For catch the sight of people, terface will be enhanced. also ll try to make th stem more conveniently and intuitivelytime first and then the reasoner does the taxonomy classifica￾tion to get the suitable cocktails from the data set. The Col￾orCocktail system takes the inferred instances of this equiva￾lent class as the final recommendation, presenting the image and direction about recommended cocktails for a user. We use RACER as our DL reasoner, moreover, we want to add common sense (?) and use common sense reasoning to enhance the system. Due to the natural relationship between emotion and common sense, we think that more complicated analysis can be done and the system should give a better result. This part of work is still on going. We may leave it as future work. Preliminary Results and Future Work After the ColorCocktail system has been preliminary work, we ask people try to use the recommender in a free way. All of them feel very interesting in such kind of system. One of the user said that she would like to use it when having a party next time and she did not need to think everything need by herself without good suggestion. She thought that the recommender can save time for preparing the foods and drinks. Furthermore, she would want the system to know the foods that she would cook, then gave her some suggestion on the beverages, especially the cocktails. Another user told us that he was boring to wait the system result showing up. The procedure of computing took too long time. He would prefer a more fast system with the closed result. The third user said that she was not familiar with wines and cocktails, so she had no idea about the accuracy of the system. But she said that this system can gave her new choice of beverage when she was going to drink something. She could know the relationship and some history of cocktails from the system, so she could try to make some different order. From the study above, the pros and cons of the work is shown. Recommending cocktail for people is a good idea because of the lack of knowledge about the drink itself. Peo￾ple need suggestions in many situation; cocktail choice is a good application. In addition, the friendliness and easiness are the basic requirements. Most users do not like a fancy￾looked system with complicated operation. So, we are trying to make a more intuitive user interface. On the other hand, ColorCocktail still need to be im￾proved. The processing time is the first issue. Users do not have patience to wait, so we need to control the procedure time. ColorCocktail takes too long time to give a recom￾mend. We’d better reduce the computational time. Besides the ‘named’ cocktails, there are still many excel￾lent cocktails without names. The kind of cocktails are pro￾vided occasionally. Bartenders give customers the recom￾mendation by both their cocktail knowledge and their expe￾rience. We want to add the experience to the ColorCocktail system. The experience representation is formal, so we need commonsense reasoning to help us to convert user. User can interact with the system intuitively. Also, commonsense rea￾soning can be used in suggestion inference. We want have more feed back about the system, hence we will put the work on Web. For catch the sight of people, interface will be enhanced. Also, we will try to make the system more conveniently and intuitively. Acknowledgements First of all, the authors would like to express their thanks to the members of the Intelligent Agents Lab at National Tai￾wan University. They have many made valuable suggestions for the design and implementation of the prototype system. This research is sponsored in part by a grant from Intel and the National Science Council of Taiwan (NSC94-2218-E- 002-057)