Search clustering
First Claim
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1. A method comprising:
- calculating a demand factor based on relationships of items and categories to query terms of search queries, the relationships established from user actions resulting from the search queries;
calculating a relevance score using the demand factor, the relevance score calculated, in part, based on a comparison of a similarity of a demand category histogram and a supply category histogram;
identifying noise data using the demand factor;
retrieving, from a plurality of listings, item data filtered from the noise data;
constructing, using a processor, at least one base cluster having at least one document with common item data stored in a suffix ordering;
compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters; and
merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data, the merged cluster being based at least in part on the demand factor.
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Abstract
In one example embodiment, a method is illustrated as including retrieving item data from a plurality of listings, the item data filtered from noise data, constructing at least one base cluster having at least one document with common item data stored in a suffix ordering, compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters, and merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data.
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Citations
23 Claims
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1. A method comprising:
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calculating a demand factor based on relationships of items and categories to query terms of search queries, the relationships established from user actions resulting from the search queries; calculating a relevance score using the demand factor, the relevance score calculated, in part, based on a comparison of a similarity of a demand category histogram and a supply category histogram; identifying noise data using the demand factor; retrieving, from a plurality of listings, item data filtered from the noise data; constructing, using a processor, at least one base cluster having at least one document with common item data stored in a suffix ordering; compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters; and merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data, the merged cluster being based at least in part on the demand factor. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer system comprising:
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at least one processor; a demand data engine to calculate a demand factor based on relationships of items and categories to query terms of search queries and further to identify noise data using the demand factor, the relationships established from user actions resulting from the search queries; a calculator to calculate a relevance score using the demand factor, the relevance score calculated, in part, based on a comparison of a similarity of a demand category histogram and a supply category histogram; a retrieving engine to retrieve, from a plurality of listings, item data filtered from the noise data; a cluster generator to construct, using the at least one processor, at least one base cluster having at least one document with common item data stored in a suffix ordering; a compacting engine to compact the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters; and a first merging engine to merge the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data, the merged cluster being based at least in part on the demand factor. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20, 21)
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22. An apparatus comprising:
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at least one processor; means for calculating a demand factor based on relationships of items and categories to query terms of search queries, the relationships established from user actions resulting from the search queries; means for calculating a relevance score using the demand factor, the relevance score calculated, in part, based on a comparison of a similarity of a demand category histogram and a supply category histogram; means for identifying noise data using the demand factor; means for retrieving, from a plurality of listings, item data filtered from the noise data; means for constructing, using the at least one processor, at least one base cluster having at least one document with common item data stored in a suffix ordering; means for compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters; and means for merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data, the merged cluster being based at least in part on a the demand factor.
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23. A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more processors of a machine cause the machine to perform operations comprising:
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calculating a demand factor based on relationships of items and categories to query terms of search queries, the relationships established from user actions resulting from the search queries; calculating a relevance score using the demand factor, the relevance score calculated, in part, based on a comparison of a similarity of a demand category histogram and a supply category histogram; identifying noise data using the demand factor; retrieving, from a plurality of listings, item data filtered from the noise data; constructing at least one base cluster having at least one document with common item data stored in a suffix ordering; compacting the at least one base cluster to create a compacted cluster representation having a reduced duplicate suffix ordering amongst the clusters; and merging the compact cluster representation to generate a merged cluster, the merging based upon a first overlap value applied to the at least one document with common item data, the merged cluster being based at least in part on the demand factor.
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Specification