초록 ▼

According to another embodiment of the invention, an approach is provided for estimating how a user would rate an item that the user has not yet rated. The approach is applicable to any type of items, including rental items such as movies and games, and the invention is not limited to any particular

According to another embodiment of the invention, an approach is provided for estimating how a user would rate an item that the user has not yet rated. The approach is applicable to any type of items, including rental items such as movies and games, and the invention is not limited to any particular type of item. The approach is applicable to a wide variety of contexts and is ideally suited for automatically selecting rental items to be recommended for rental. One or more items that have been rated by the user are identified. Then, one or more other users are identified that have rated the one or more items and given ratings to the one or more items that are substantially similar to ratings given by user to the one or more items. Finally, an estimation is made how the user would rate the item that the user has not yet rated based upon how the one or more other users rated the item.

대표청구항 ▼

What is claimed is: 1. A computer-implemented method for estimating how a user would rate a movie that the user has not yet rated, the method comprising: in a computer system identifying a set of common movies that have been rated favorably by the user and that have ratings that satisfy a minimum r

What is claimed is: 1. A computer-implemented method for estimating how a user would rate a movie that the user has not yet rated, the method comprising: in a computer system identifying a set of common movies that have been rated favorably by the user and that have ratings that satisfy a minimum rating threshold; in the computer system identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are substantially similar to ratings given by the user for the set of common movies; and in the computer system estimating how the user would rate the movie that the user has not yet rated by determining a similarity between the set of common movies and the movie that the user has not yet rated, wherein determining the similarity between the set of common movies and the movie that the user has not yet rated includes comparing the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated. 2. The computer-implemented method as recited in claim 1, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the common movies that are substantially similar to ratings given by the user for the set of common movies includes identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies. 3. The computer-implemented method as recited in claim 2, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies includes for each movie from the set of common movies, determining whether the rating given by the one or more other users is within a specified amount of the rating given by the user. 4. The computer-implemented method as recited in claim 2, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies includes determining whether an average of the ratings given by the one or more other users for the set of common movies is within a specified amount of an average of the ratings given by the user for the set of common movies. 5. The computer-implemented method as recited in claim 1, wherein the comparing the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated includes comparing an average of the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated. 6. The computer-implemented method as recited in claim 1, wherein the number of movies in the set of common movies is at least a specified number of movies. 7. A computer-readable storage medium for estimating how a user would rate a movie that the user has not yet rated, the computer-readable medium carrying instructions which, when executed by one or more processors: identify a set of common movies that have been rated favorably by the user and that have ratings that satisfy a minimum rating threshold; identify one or more other users that have rated the set of common movies and given ratings for the set of common movies that are substantially similar to ratings given by the user for the set of common movies; and estimate how the user would rate the item that the user has not yet rated by determining a similarity between the set of common movies and the item that the user has not yet rated, wherein determining the similarity between the set of common movies and the movie that the user has not yet rated includes by-comparing the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie-that the user has not yet rated. 8. The computer-readable medium as recited in claim 7, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are substantially similar to ratings given by the user for the set of common movies includes identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies. 9. The computer-readable medium as recited in claim 8, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies includes for each movie from the set of common movies, determining whether the rating given by the one or more other users is within a specified amount of the rating given by the user. 10. The computer-readable medium as recited in claim 8, wherein the identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are within a specified amount of ratings given by the user for the set of common movies includes determining whether an average of the ratings given by the one or more other users for the set of common movies is within a specified amount of an average of the ratings given by the user for the set of common movies. 11. The computer-readable medium as recited in claim 7, wherein the comparing the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated includes comparing an average of the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated. 12. The computer-readable medium as recited in claim 7, wherein the number of movies in the set of common movies is at least a specified number of movies. 13. An apparatus comprising: a memory configured to store data; and an estimator mechanism communicatively coupled to the memory and configured to estimate how a user would rate a movie that the user has not yet rated by identifying a set of common movies that have been rated favorably by the user and that have ratings that satisfy a minimum rating threshold, identifying one or more other users that have rated the set of common movies and given ratings for the set of common movies that are substantially similar to ratings given by the user for the set of common movies, and estimating how the user would rate the item that the user has not yet rated by determining a similarity between the set of common movies and the movie that the user has not yet rated, wherein determining the similarity between the set of common movies and the movie that the user has not yet rated includes by comparing the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated. 14. The apparatus as recited in claim 13, wherein the estimator mechanism is further configured to determine whether the ratings given by the one or more other users for the set of common movies are within a specified amount of the ratings given by the user for the set of common movies. 15. The apparatus as recited in claim 14, wherein the estimator mechanism is further configured to determine, for each movie from the set of common movies, whether the rating given by the one or more other users is within a specified amount of the rating given by the user. 16. The apparatus as recited in claim 14, wherein the estimator mechanism is further configured to determine whether an average of the ratings given by the one or more other users for the set of common movies is within a specified amount of an average of the ratings given by the user for the set of common movies. 17. The apparatus as recited in claim 13, wherein the estimator mechanism is further configured to compare an average of the ratings given by the one or more other users for the set of common movies to ratings given by the one or more other users for the movie that the user has not yet rated. 18. The apparatus as recited in claim 13, wherein the number of movies-in the set of common movies is at least a specified number of movies.