System and process for automatically providing fast recommendations using local probability distributions
First Claim
1. A system for automatically determining a set of at least one maximal utility object from a set of at least one object represented by a probabilistic model, comprising:
- determining an upper bound for the utility of each object;
sorting the objects by the upper bounds in order of highest to lowest;
obtaining a set of known object values for a particular entity;
using the probabilistic model in combination with the information known about an entity to begin predicting the set of maximal utility objects from the set of objects;
examining the utilities associated with each object in the set of objects in the sorted order for selecting maximal utility objects until the set of maximal utility objects is full; and
continuing the examination of utilities until the utility associated with a lowest utility object in the set of maximal utility objects is greater than the upper bound of the utility of a next sorted object in the set of objects.
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Abstract
The system and method of the present invention automatically extracts the top k recommendations of objects, such as topics, items, products, books, movies, food, drinks, etc., from a local probabilistic recommendation system. Unlike prior systems, the present invention accomplishes the extraction of the top k recommendations of objects without examining a probability for every object that can be recommended. Further, the system and method of the present invention is capable of being implemented using probabilistic recommendation systems based on any conventional type of probabilistic distribution or machine learning technique, including, for example, decision trees and Bayesian networks.
32 Citations
23 Claims
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1. A system for automatically determining a set of at least one maximal utility object from a set of at least one object represented by a probabilistic model, comprising:
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determining an upper bound for the utility of each object; sorting the objects by the upper bounds in order of highest to lowest; obtaining a set of known object values for a particular entity; using the probabilistic model in combination with the information known about an entity to begin predicting the set of maximal utility objects from the set of objects; examining the utilities associated with each object in the set of objects in the sorted order for selecting maximal utility objects until the set of maximal utility objects is full; and continuing the examination of utilities until the utility associated with a lowest utility object in the set of maximal utility objects is greater than the upper bound of the utility of a next sorted object in the set of objects. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-readable medium having computer executable instructions for dynamically extracting at least one highest probability object recommendation from a probabilistic model without examining all possible probabilistic recommendations from the probabilistic model, said computer executable instructions comprising:
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extracting upper bounds of particular states of probability distributions for objects represented by the probabilistic model; sorting the upper bounds in order of highest to lowest; examining objects represented by the probabilistic model in order of the sorted upper bounds for each object for determining at least one highest probability object recommendation; and terminating the examination of the objects as soon as the upper bound of the lowest probability recommended object is greater than an upper bound of a next sorted object. - View Dependent Claims (11, 12, 13, 14, 15, 16)
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17. A method for determining at least one highest probability recommendation from a probabilistic model, said model representing at least one object using a probability distribution for representing each object, comprising:
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determining an upper bound of a particular state of the probability distribution representing each object; sorting each object represented by the probabilistic model by sorting the upper bounds associated with each object in order of highest to lowest; determining a set of user preferences for a particular user; extracting at least one highest probability recommendation from the probabilistic model based on the set of user preferences for the particular user, wherein the objects represented by the model are examined in the sorted order for extracting the at least one highest probability recommendation; and terminating the examination of the objects and the extraction of highest probability recommendations as soon as a lowest upper bound of any of the highest probability recommendations is greater than an upper bound of a next sorted object. - View Dependent Claims (18, 19, 20, 21, 22, 23)
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Specification