A personal taste assessment system recommends and predicts a person's preference for a consumable or other item. The system accesses a user profile for a person. The user profile includes a preference model representing associations between the person's ratings of items and a set of item characteristics. The system also accesses a database of characteristic values for a group of items, uses the identifying information to identify a candidate item having characteristic values whose properties match characteristics associated with the rated items that the person found to be appealing, and processes the characteristic values of the candidate item with the user profile to generate a predicted rating as a prediction of how the person would rate the identified candidate item. The system then causes an electronic device to output an identification of the candidate item and the predicted rating.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of generating a recommendation for a consumable item, comprising: by one or more processors: accessing a user profile for a person, wherein the user profile comprises a preference model representing associations between the person's ratings of items and a plurality of item characteristics; accessing a database of characteristic values for a plurality of candidate items; using the identifying information to identify a candidate item having characteristic values whose properties match characteristics associated with the rated items that the person found to be appealing; processing the characteristic values of the candidate item with the user profile to generate a predicted rating as a prediction of how the person would rate the identified candidate item; causing an electronic device to output an identification of the identified candidate item and the predicted rating. 2. The method of claim 1, wherein using the identifying information to identify a candidate item also comprises constraining the plurality of candidate items to those having characteristics that satisfy a user-supplied criterion. 3. The method of claim 1, wherein using the identifying information to identify a candidate item also comprises constraining the plurality of candidate items to those having characteristics that satisfy a situational criterion. 4. The method of claim 1, wherein the situational criterion comprises available inventory at a retailer. 5. The method of claim 1, wherein: each of the candidate items is a consumable item; and the characteristic values of the identified candidate item comprise values of traits that are associated with at least one of the following senses: taste, smell and touch. 6. The method of claim 1, wherein: the characteristic values of the identified candidate item correspond to one or more indicators of preference in the preference model; and generating the predicted rating comprises: entering values of the retrieved characteristics of the identified candidate item into the preference model, and returning a result of the preference model as the prediction. 7. The method of claim 1, wherein processing the retrieved characteristics with the user profile to generate the predicted rating comprises: analyzing dependency patterns in the preference model of the user profile; and using the dependency patterns to generate the predicted rating. 8. The method of claim 6, wherein analyzing the dependency patterns comprises: analyzing consistent patterns of dependency in the preference model and identifying a representative rating based on the consistent patterns; and using the representative rating as the predicted rating. 9. The method of claim 6, wherein analyzing the dependency patterns comprises: analyzing contrasting or polarizing patterns of dependency in the preference model; processing values of characteristics of the identified candidate item in the preference model having contrasting or polarizing patterns of dependency to yield a result; and using the result as the predicted rating. 10. The method of claim 1, wherein processing the retrieved characteristics with the user profile to generate the predicted rating comprises: determining that no preference model in the user profile can be matched with, or is reflective of, the characteristics of the identified candidate item; and generating an inconclusive result as the predicted rating. 11. A system for predicting a person's preference for an item, comprising: one or more processors: a database of characteristic values for a plurality of candidate items; a computer-readable memory storing a user profile for a person, wherein the user profile comprises a preference model representing associations between the person's ratings of items and a plurality of item characteristics; and a computer-readable memory containing programming instructions that, when executed, cause one or more of the processors to: access a user profile for a person, wherein the user profile comprises a preference model representing associations between the person's ratings of items and a plurality of item characteristics, access a database of characteristic values for a plurality of candidate items, use the identifying information to identify a candidate item having characteristic values whose properties match characteristics associated with the rated items that the person found to be appealing, process the characteristic values of the candidate item with the user profile to generate a predicted rating as a prediction of how the person would rate the identified candidate item, and cause an electronic device to output an identification of the identified candidate item and the predicted rating. 12. The system of claim 11, wherein the instructions to use the identifying information to identify a candidate item also comprise instructions to constrain the plurality of candidate items to those having characteristics that satisfy a user-supplied criterion. 13. The system of claim 11, wherein the instructions to use the identifying information to identify a candidate item also comprise instructions to constrain the plurality of candidate items to those having characteristics that satisfy a situational criterion. 14. The method of claim 12, wherein the situational criterion comprises available inventory at a retailer. 15. The system of claim 11, wherein: each of the candidate items is a consumable item; and the characteristic values of the identified candidate item comprise values of traits that are associated with at least one of the following senses: taste, smell and touch. 16. The system of claim 11, wherein: the characteristic values of the identified candidate item correspond to one or more indicators of preference in the preference model; and the instructions to generate the predicted rating comprise instructions to: enter values of the retrieved characteristics of the identified candidate item into the preference model, and return a result of the preference model as the prediction. 17. The system of claim 11, wherein the instructions to process the retrieved characteristics with the user profile to generate the predicted rating comprise instructions to: analyze dependency patterns in the preference model of the user profile; and use the dependency patterns to generate the predicted rating. 18. The system of claim 17, wherein the instructions to analyze the dependency patterns comprise instructions to: analyze consistent patterns of dependency in the preference model and identifying a representative rating based on the consistent patterns; and use the representative rating as the predicted rating. 19. The system of claim 17, wherein the instructions to analyze the dependency patterns comprise instructions to: analyze contrasting or polarizing patterns of dependency in the preference model; process values of characteristics of the identified candidate item in the preference model having contrasting or polarizing patterns of dependency to yield a result; and use the result as the predicted rating. 20. The system of claim 11, wherein the instructions to process the retrieved characteristics with the user profile to generate the predicted rating comprise instructions to: determine that no preference model in the user profile can be matched with, or is reflective of, the characteristics of the identified candidate item; and generate an inconclusive result as the predicted rating.