Method and device for information retrieval
First Claim
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1. A method of information retrieval, comprising:
- determining, using a microprocessor, Q generative models (λ
) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training;
receiving a user query (q);
choosing, using said microprocessor, N generative models out of the Q generative models, with N<
Q; and
determining, using said microprocessor, a content item (d) based on said query and a combination of the N generative models, whereinsaid step of choosing includes determining, for each of the Q generative models, a value based on a quality estimation function, wherein the N generative models are chosen depending on the value,the quality estimation function depends on the query, such that the choosing of the N generative models depends on the query, wherein the step of choosing is performed at runtime when the query is received, andthe query comprises a plurality of N words w1, . . . , w−
N, and the quality estimation function is given by
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Abstract
A method of information retrieval that includes determining Q generative models (λ) in accordance with Probabilistic Latent Semantic Indexing (PLSI). The Q generative models are determined in offline training. The method also includes receiving a user query (q), choosing N generative models out of the Q generative models, and determining a content item (d) based on the query and a combination of the N generative models.
7 Citations
13 Claims
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1. A method of information retrieval, comprising:
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determining, using a microprocessor, Q generative models (λ
) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training;receiving a user query (q); choosing, using said microprocessor, N generative models out of the Q generative models, with N<
Q; anddetermining, using said microprocessor, a content item (d) based on said query and a combination of the N generative models, wherein said step of choosing includes determining, for each of the Q generative models, a value based on a quality estimation function, wherein the N generative models are chosen depending on the value, the quality estimation function depends on the query, such that the choosing of the N generative models depends on the query, wherein the step of choosing is performed at runtime when the query is received, and the query comprises a plurality of N words w1, . . . , w−
N, and the quality estimation function is given by - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A device comprising:
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a microprocessor adapted to calculate Q generative models (λ
) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training;a storage adapted to store said Q generative models; an input interface adapted to receive a user query (q); and a selection unit adapted to select N generative models out of the Q generative models, with N<
Q;wherein said microprocessor is further adapted to determine a content item (d) based on said query and a combination of the N generative models, wherein said selection unit is further adapted to determine, at runtime when a query has been received by the input interface, for each of the Q generative models, a value based on a quality estimation function, the N generative models are chosen depending on the value, the query comprises a plurality of N words w1, . . . , w−
N, andthe quality estimation function is given by - View Dependent Claims (11, 12)
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13. A television equipment comprising:
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a data processor adapted to calculate Q generative models (λ
) in accordance with Probabilistic Latent Semantic Indexing (PLSI), said Q generative models being determined in an offline training, wherein a training material for said offline training comprises EPG data;a storage adapted to store said Q generative models; an input interface adapted to receive a user query (q), the user query being related to television program information; and a selection unit adapted to select N generative models out of the Q generative models, with N<
Q, whereinsaid data processor is further adapted to determine a content item (d) based on said query and a combination of the N generative models, said content item being related to a television program, wherein said selection unit is further adapted to determine, at runtime when a query has been received by the input interface, for each of the Q generative models, a value based on a quality estimation function, the N generative models are chosen depending on the value, the query comprises a plurality of N words w1, . . . , w−
N, andthe quality estimation function is given by
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Specification
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Current AssigneeSony Corporation (Sony Group Corp.)
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Original AssigneeSony Corporation (Sony Group Corp.)
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InventorsKemp, Thomas
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Primary Examiner(s)Gaffin, Jeffrey A
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Assistant Examiner(s)Traktovenko, Ilya
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Application NumberUS13/114,509Publication NumberTime in Patent Office1,015 DaysField of Search706/12, 706/62US Class Current706/12CPC Class CodesG06F 16/3346 using probabilistic modelH04N 21/4756 for rating content, e.g. sc...
