Providing recommendations using information determined for domains of interest
First Claim
1. A computer-implemented method for providing information based on automatically determined relationships, the method comprising:
- under control of one or more computing systems configured to provide a relevance determination service, automatically determining relevant information to recommend by,automatically analyzing contents of a plurality of documents related to a first domain of interest to identify multiple inter-term relationships between at least some of a plurality of terms that are present in the contents of the documents, each of the identified relationships indicating an initial assessed relevance between at least one of the terms and at least one other of the terms;
automatically generating a term relevance neural network that models the assessed relevances of the identified relationships, the term relevance neural network initially modeling the assessed initial relevances, and repeatedly updating the assessed relevances that are modeled by the term relevance neural network based on feedback obtained from users that perform selections corresponding to the plurality of terms;
automatically generating a probabilistic Bayesian network based on the updated assessed relevances of at least some of the identified relationships, the probabilistic Bayesian network including information that indicates probabilities for relationships between at least some of the plurality of terms; and
using the information included in the probabilistic Bayesian network to provide recommendations related to the first domain by, for each of multiple users;
obtaining information about a first group of one or more of the plurality of terms for which the user has expressed a preference;
for each of one or more target terms of the plurality of terms that are not in the first group, automatically determining a probability that the target term is an unexpressed preference of the user, the determined probability being based on the preference of the user for the one or more terms of the first group and being based on one or more relationships between the one or more terms of the first group and the target term that are indicated in the information included in the probabilistic Bayesian network; and
providing one or more recommendations for the user related to the first domain that are based on a selected second group of at least one of the target terms, the target terms of the second group being selected based on the determined probabilities that those target terms are unexpressed preferences of the user, and wherein the target terms of the selected second group for at least one of the multiple users differ from the target terms of the selected second group for at least one other of the multiple users.
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Abstract
Techniques are described for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. Such automatically determined information may then be used to assist users in obtaining information from the domain that is of interest (e.g., documents with contents that are relevant to user-specified terms and/or to other terms that are determined to be sufficiently related to the user-specified terms). For example, recommendations may be automatically generated for a user by using information about specified preferences or other interests of the user with respect to one or more terms and identifying other particular terms that are sufficiently probable to be of interest to that user, such as based on a generated probabilistic representation of relationships between particular terms for the domain.
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Citations
46 Claims
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1. A computer-implemented method for providing information based on automatically determined relationships, the method comprising:
under control of one or more computing systems configured to provide a relevance determination service, automatically determining relevant information to recommend by, automatically analyzing contents of a plurality of documents related to a first domain of interest to identify multiple inter-term relationships between at least some of a plurality of terms that are present in the contents of the documents, each of the identified relationships indicating an initial assessed relevance between at least one of the terms and at least one other of the terms; automatically generating a term relevance neural network that models the assessed relevances of the identified relationships, the term relevance neural network initially modeling the assessed initial relevances, and repeatedly updating the assessed relevances that are modeled by the term relevance neural network based on feedback obtained from users that perform selections corresponding to the plurality of terms; automatically generating a probabilistic Bayesian network based on the updated assessed relevances of at least some of the identified relationships, the probabilistic Bayesian network including information that indicates probabilities for relationships between at least some of the plurality of terms; and using the information included in the probabilistic Bayesian network to provide recommendations related to the first domain by, for each of multiple users; obtaining information about a first group of one or more of the plurality of terms for which the user has expressed a preference; for each of one or more target terms of the plurality of terms that are not in the first group, automatically determining a probability that the target term is an unexpressed preference of the user, the determined probability being based on the preference of the user for the one or more terms of the first group and being based on one or more relationships between the one or more terms of the first group and the target term that are indicated in the information included in the probabilistic Bayesian network; and providing one or more recommendations for the user related to the first domain that are based on a selected second group of at least one of the target terms, the target terms of the second group being selected based on the determined probabilities that those target terms are unexpressed preferences of the user, and wherein the target terms of the selected second group for at least one of the multiple users differ from the target terms of the selected second group for at least one other of the multiple users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A computer-implemented method for providing information based on automatically determined relationships, the method comprising:
under control of one or more computing systems configured to provide recommendation information based on automatically determined relationships between terms; receiving one or more indications of a group of multiple content items whose contents are representative of a subject area of interest, the contents including a plurality of terms; automatically analyzing, by the configured one or more computing systems, the multiple content items of the group to identify relationships between at least some of the plurality of terms, wherein the relationships identified by the automatic analyzing include multiple inter-term relationships that are each between at least two of the plurality of terms, wherein the automatic analyzing further includes automatically assessing for each of the multiple inter-term relationships an initial degree of relevance between the at least two terms for the inter-term relationship, wherein a first of the identified multiple inter-term relationships has an assessed degree of relevance of one or more first terms of the plurality of terms to one or more other second terms of the plurality of terms, and wherein the automatic analyzing of the multiple content items of the group further includes generating probabilistic information for the first inter-term relationship that indicates a likelihood of a relationship between the one or more first terms and the one or more other second terms; obtaining information about one or more indicated terms for which a first user has a preference, the indicated terms including at least one of the first terms but not including any of the second terms; for each of one or more of the second terms, automatically determining, by the configured one or more computing systems, a likelihood that the second term is of interest to the first user based at least in part on the at least one term included in the indicated terms and on the assessed degree of relevance of the one or more first terms to the one or more second terms, wherein the automatic determining of the likelihood for each of the one or more second terms is based at least in part on use of the generated probabilistic information; and providing an indication of at least one of the one or more second terms that is selected to enable one or more recommendations to be provided to the first user based on the at least one second terms, the at least one second terms being selected based on one or more determined criteria for assessing the determined likelihoods of the at least one second terms. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer-readable storage medium having stored contents that configure a computing system of a relevance determination system to provide information based on automatically determined relationships, by performing a method comprising:
under control of the configured computing system, automatically analyzing, by the configured computing system, contents of multiple related content items in order to identify relationships between at least some of a plurality of terms included in the contents, wherein a first of the identified relationships indicates an assessed relevance of a first term of the plurality of terms to one or more other second terms of the plurality of terms, wherein the contents of the multiple related content items are representative of a subject area of interest; obtaining information about one or more indicated terms of interest to a first user, wherein the indicated terms include the first term but not including any of the second terms, and wherein the one or more terms are indicated by the first user to be preferences of the first user; automatically determining, by the configured computing system, a likelihood that a selected one of the second terms is of interest to the first user based at least in part on the assessed relevance of the first term to the one or more second terms, wherein the automatic determining of the likelihood is performed for each of the one or more other second terms; and providing an indication of the selected one second term and of the determined likelihood to enable one or more suggestions to be determined for the first user based on the one second term, wherein the selected one second term is selected based on one or more criteria for assessing the determined likelihoods of the one or more other second terms, and wherein the providing of the indication of the selected one second term and of the determined likelihood includes determining the one or more suggestions based on the selected one second term and providing at least one of the determined suggestions as a recommendation to the first user. - View Dependent Claims (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41)
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42. A computing system configured to provide information based on automatically determined relationships, comprising:
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one or more processors; and a relevance determination system that is configured to, when executed by at least one of the one or more processors, provide information based on automatically determined relationships by; automatically analyzing multiple content items that are representative of a subject area of interest in order to identify inter-term relationships between a plurality of terms included in contents of the multiple content items, and wherein each of the inter-term relationships indicates an assessed relevance of at least one first term of the plurality of terms to at least one other second term of the plurality of terms; automatically generating a probabilistic representation of selected inter-term relationships based at least in part on the assessed relevances for the selected inter-term relationships, the probabilistic representation including information related to a determined likelihood of a relationship between the at least one first term and the at least one second term for each of the selected inter-term relationships; and providing information about the determined likelihood of the relationship between the at least one first term and the at least one second term for at least one of the selected inter-term relationships to enable one or more suggestions to be determined for a user who has an interest in the at least one first term for the at least one selected inter-term relationship, wherein the providing of the information about the determined likelihood of the relationship between the at least one first term and the at least one second term for the at least one selected inter-term relationship includes; after obtaining information about the user having a preference for the at least one first term of the at least one selected inter-term relationship, using the information included in the generated probabilistic representation to automatically determine that the at least one second term of the at least one selected inter-term relationship is also of interest to the user based at least in part on the determined likelihood for the at least one selected inter-term relationship; determining the one or more suggestions for the user based at least in part on the at least one second term of the at least one selected inter-term relationship; and providing the determined one or more suggestions for the user. - View Dependent Claims (43, 44)
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45. A system configured to provide information based on automatically determined relationships, comprising:
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one or more processors of one or more computing systems; a relevance determination system that is configured to, when executed by at least one of the one or more processors, provide information based on automatically determined relationships by; automatically analyzing multiple content items related to a subject area of interest in order to identify inter-term relationships between a plurality of terms related to the multiple content items, each of the inter-term relationships indicating an assessed relevance of at least one first term of the plurality of terms to at least one other second term of the plurality of terms; automatically generating a probabilistic representation of selected inter-term relationships based at least in part on the assessed relevances for the selected inter-term relationships, the probabilistic representation including information related to a determined likelihood of a relationship between the at least one first term and the at least one second term for each of the selected inter-term relationships; and providing information about the determined likelihood of the relationship between the at least one first term and the at least one second term for at least one of the selected inter-term relationships to enable one or more suggestions to be determined for a user who has an interest in the at least one first term for the at least one selected inter-term relationship; and one or more additional systems configured to, when executed by at least one of the one or more processors, receive the provided information about the determined likelihood of the relationship between the at least one first term and the at least one second term for at least one of the selected inter-term relationships, and to, for each of multiple users; obtain information about one or more terms indicated by the user to be preferences of the user; automatically determine one or more second terms that are of likely interest to the user based at least in part on the received provided information; automatically determine one or more recommendations for the user based at least in part on the determined one or more second terms; and provide the determined one or more recommendations to the user. - View Dependent Claims (46)
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Specification