Methods and apparatus for predicting and selectively collecting preferences based on personality diagnosis
First Claim
1. A method for using a machine to predict a value of an attribute, having no assigned value, of an active entity, the method comprising:
- a) accepting, with the machine, values of attributes of a number of other entities;
b) generating, with the machine, for each of the other entities, a probability that the active entity'"'"'s true personality type is that of the current other entity;
c) determining, with the machine, for each possible value of the attribute having no assigned value, a probability that the active entity values the attribute with the current possible value based, at least in part, on the probabilities that the active entity has a true personality type which is the same as that of the other entities as generated in act (b); and
d) selecting, with the machine, from among the possible values of the attribute having no assigned value, the possible value with the maximum probability determined in act (c) to generate a predicted value.
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Accused Products
Abstract
A new recommendation technique, referred to as “personality diagnosis”, that can be seen as a hybrid between memory-based and model-based collaborative filtering techniques, is described. Using personality diagnosis, all data may be maintained throughout the processes, new data can be added incrementally, and predictions have meaningful probabilistic semantics. Each entity'"'"'s (e.g., user'"'"'s) reported attributes (e.g., item ratings or preferences) may be interpreted as a manifestation of their underlying personality type. Personality type may be encoded simply as a vector of the entity'"'"'s (e.g., user'"'"'s) “true” values (e.g., ratings) for attributes (e.g., items) in the database. It may be assumed that entities (e.g., users) report values (e.g., ratings) with a distributed (e.g., Gaussian) error. Given an active entity'"'"'s (e.g., user'"'"'s) known attribute values (e.g., item ratings), the probability that they have the same personality type as every other entity (e.g., user) may be determined. Then, the probability that they will have a given value (e.g., rating) for a valueless (e.g., unrated) attribute (e.g., item) may then be determined based on the entity'"'"'s (e.g., user'"'"'s) personality type. The probabilistic determinations may be used to determine expected value of information. Such an expected value of information could be used in at least two ways. First, an interactive recommender could use expected value of information to favorably order queries for attribute values (e.g., item ratings), thereby mollifying what could otherwise be a tedious and frustrating process. Second, expected value of information could be used to determine which entries of a database to prune or ignore—that is, which entries, which if removed, would have a minimal effect of the accuracy of recommendations.
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Citations
24 Claims
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1. A method for using a machine to predict a value of an attribute, having no assigned value, of an active entity, the method comprising:
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a) accepting, with the machine, values of attributes of a number of other entities;
b) generating, with the machine, for each of the other entities, a probability that the active entity'"'"'s true personality type is that of the current other entity;
c) determining, with the machine, for each possible value of the attribute having no assigned value, a probability that the active entity values the attribute with the current possible value based, at least in part, on the probabilities that the active entity has a true personality type which is the same as that of the other entities as generated in act (b); and
d) selecting, with the machine, from among the possible values of the attribute having no assigned value, the possible value with the maximum probability determined in act (c) to generate a predicted value. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 18)
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13. A method for using a machine to generate, for each of a number of personality types defined by a plurality of other entities, probabilities that an active entity is that personality type, the method comprising:
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a) accepting, with the machine, values of attributes associated with a number of other entities; and
b) for each of the other entities, i) determining, with the machine, for each attribute, a probability that the active entity values the attribute given that the active entity'"'"'s true value of the attribute is the same as that of the current other entity, and ii) determining, with the machine, a probability that the active entity'"'"'s true personality type is that of the current other entity. - View Dependent Claims (14, 15, 19)
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16. A method for using a machine to determine, for each possible value of an attribute having no assigned value, a probability that an active entity values the attribute with the current possible value, the method comprising:
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a) accepting, with the machine, for each of a number of personality types, a probability that the active user is of the current personality type;
b) determining, with the machine, a probability that the active entity values the attribute with the current possible value based on i) for each personality type, a probability that the active entity values the current unknown attribute with the current value given that the active user is of the current personality type, and ii) a probability that the active user is of the current personality type. - View Dependent Claims (17, 20)
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21. An apparatus for predicting the value of an attribute of an active entity, the apparatus comprising:
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a) a personality type generator for generating, for each of a plurality of personality types, a probability that the active entity is of the current personality type; and
b) an attribute value predictor for predicting the value of the attribute of the active entity based on the each of the probabilities that the active entity is of each of the personality types. - View Dependent Claims (22, 23, 24)
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Specification