Abstract: This document describes a method and system for recommending items, such as beverages, that members of a group are likely to find appealing. When group members are identified, the system may identify one or more preference models for each member. Each preference model represents a pattern of dependency between characteristics of items that the member has rated and the member's ratings for those items. The system may develop a group preference profile by merging the patterns of dependency for each of the members into a group preference model. Then, when it receives a request for a recommendation for an item, the system uses the group preference profile to select, from a database, a candidate item having characteristics which are likely to appeal to many or all members of the group.

Abstract: A personal taste assessment and prediction system implements a method of predicting a person's preference for an item. The system may receive identifying information about an item of interest, and it may use the identifying information to access a database of characteristic values for various items and identify one or more characteristics of the item of interest. The system will access a user profile and process the retrieved characteristics with a user profile for a person to generate a predicted rating for the item of interest for the person.

Abstract: A personal taste assessment system receives a person's ratings for various rated items, such as wines or other beverages, and it develops a preference model for the person based on the received ratings. When developing the preference model, the system accesses a database of candidate items, wherein each candidate item is associated with at least one trait value. The system retrieves from the database a value for at least one trait for each of the rated items, identifies at least one pattern of dependency between at least one of the retrieved trait values and at least one of the received ratings, and determines a preference model based on the at least one pattern of dependency. The system may use the preference model to develop a preference profile for the person, make recommendations, and/or make predictions as to items that the person and/or a group that includes the person may enjoy.

Abstract: A personal taste assessment and prediction system implements a method of predicting a person's preference for an item. The system may receive identifying information about an item of interest, and it may use the identifying information to access a database of characteristic values for various items and identify one or more characteristics of the item of interest. The system will access a user profile and process the retrieved characteristics with a user profile for a person to generate a predicted rating for the item of interest for the person.

Abstract: This document describes a method and system for recommending items, such as beverages, that members of a group are likely to find appealing. When group members are identified, the system may identify one or more preference models for each member. Each preference model represents a pattern of dependency between characteristics of items that the member has rated and the member's ratings for those items. The system may develop a group preference profile by merging the patterns of dependency for each of the members into a group preference model. Then, when it receives a request for a recommendation for an item, the system uses the group preference profile to select, from a database, a candidate item having characteristics which are likely to appeal to many or all members of the group.

Abstract: A personal taste assessment system receives a person's ratings for various rated items, such as wines or other beverages, and it develops a preference model for the person based on the received ratings. When developing the preference model, the system accesses a database of candidate items, wherein each candidate item is associated with at least one trait value. The system retrieves from the database a value for at least one trait for each of the rated items, identifies at least one pattern of dependency between at least one of the retrieved trait values and at least one of the received ratings, and determines a preference model based on the at least one pattern of dependency. The system may use the preference model to develop a preference profile for the person, make recommendations, and/or make predictions as to items that the person and/or a group that includes the person may enjoy.