Distinguishing accessories from products for ranking search results

Abstract

Offer listings can be classified as accessory offers or product offers using a classification operation performed on a corpus of offers. Data from the classification operation can be used to classify received queries as either product or accessory, and to classify results as products or accessories for purposes of presenting a relevant list of results to a user.

1 . A computer implemented method comprising:
 receiving a query from a client device;   identifying a set of ranked results corresponding to the query from a corpus of offer data;   classifying the query as a product query or an accessory query;   if the query is classified as a product query:
 for each result in the set of ranked results, determining if the result is an accessory result; 
 if the result is an accessory result, demoting the rank of the result in a set of modified ranked results; and 
 sending to the client device at least a subset of highest ranked results in the modified ranked results in response to the query. 
   
     
     
         2 . The method of  claim 1 , wherein determining if a result is an accessory result comprises:
 comparing keywords of the result to an accessory blacklist; and   classifying the result as an accessory result if keywords of the result are found in the accessory blacklist.   
     
     
         3 . The method of  claim 1 , wherein:
 determining if a result is an accessory result comprises determining likelihood score based on query keywords and keyword price distribution data determined from the corpus of offer data.   
     
     
         4 . The method of  claim 3 , wherein the likelihood score is a log likelihood ratio score of product prices and accessory prices. 
     
     
         5 . The method of  claim 4 , wherein the likelihood ratio score is determined according to:
     P   TOTAL =log( Pr [price|keyword 1 ,accessory])−log( Pr [price|keyword 1 ,product])+log( Pr [price|keyword 2 ,accessory])−log( Pr [price|keyword 2 ,product]) . . . log( Pr [price|keyword n ,accessory])−log( Pr [price|keyword n ,product]),   wherein:
 keyword 1 , keyword 2 , through keyword n , are the query keywords; 
   Pr [price|keyword n , product] is a probability that a given result is a product result based on a result price and a product price mean and product price standard deviation of an n th  keyword in the query;   Pr [price|keyword n , accessory] is a probability that a given result is an accessory result based on the result price and an accessory price mean and an accessory price standard deviation of the n th  keyword in the query; and   the result is determined to be an accessory result if P TOTAL  is greater than a threshold score.   
     
     
         6 . The method of  claim 3 , further comprising:
 determining the keyword price distribution data from the corpus of offer data.   
     
     
         7 . The method of  claim 6 , wherein determining the keyword price distribution data from the corpus of offer data comprises:
 classifying offers in the corpus of offer data as product offers and accessory offers;   determining a product price mean and a product price standard deviation for offer keywords of product offers; and   determining an accessory price mean and an accessory price standard deviation for offer keywords of accessory offers.   
     
     
         8 . The method of  claim 6 , wherein determining the keyword price distribution data from the corpus of offer data comprises:
 heuristically classifying offers in the corpus of offer data as product offers and accessory offers;   determining price distributions for offer keywords corresponding to offers classified as accessory offers;   determining price distributions for offer keywords corresponding to offers classified as product offers; and   reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.   
     
     
         9 . The method of  claim 8 , wherein determining the keyword price distribution data from the corpus of offer data further comprises:
 until a number of reclassified offers is below a threshold number:
 determining price distributions for offer keywords corresponding to offers classified as accessory offers; 
 determining price distributions for offer keywords corresponding to offers classified as product offers; and 
 reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions. 
   
     
     
         10 . The method of  claim 8 , wherein the price distributions are Gaussian distributions. 
     
     
         11 . The method of  claim 1 , wherein classifying the query as a product query or an accessory query comprises:
 comparing query keywords to an accessory blacklist;   classifying the query as an accessory query if query keywords are found in the accessory blacklist.   
     
     
         12 . The method of  claim 1 , wherein classifying the query as a product query or an accessory query comprises:
 if a threshold number of results are classified as accessory offers according to a classification operation, classifying the query as an accessory query.   
     
     
         13 . The method of  claim 12 , wherein the previous classification operation comprises:
 heuristically classifying offers in the corpus of offer data as product offers and accessory offers;   determining price distributions for offer keywords corresponding to offers classified as accessory offers;   determining price distributions for offer keywords corresponding to offers classified as product offers; and   reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.   
     
     
         14 . A system comprising:
 one or more processing devices; and   software stored on a computer readable medium comprising instructions executable by the one or more processing devices and upon such execution cause the one or more processing devices to perform operations comprising:
 receive a query; 
 identify a set of ranked results corresponding to the query from a corpus of offer data; 
 classify the query as a product query or an accessory query; 
 if the query is classified as a product query:
 for each result in the set of ranked results, determining if the result is an accessory result; and 
 if the result is an accessory result, demoting the rank of the result in a set of modified ranked results, and 
 
   the front end processor further operable to send at least a subset of highest ranked results in the modified ranked results to the client device in response to the query.   
     
     
         15 . The system of  claim 14 , wherein determining if a result is an accessory result comprises:
 comparing keywords of the result to an accessory blacklist; and   classifying the result as an accessory result if keywords of the result are found in the accessory blacklist.   
     
     
         16 . The method of  claim 14 , wherein:
 determining if a result is an accessory result comprises determining a likelihood score based on query keywords and keyword price distribution data determined from the corpus of offer data.   
     
     
         17 . The method of  claim 16 , wherein the likelihood score is a log likelihood ratio score of products prices and accessory prices. 
     
     
         18 . The method of  claim 17 , wherein the likelihood ratio score is determined according to:
     P   TOTAL =log( Pr [price|keyword 1 ,accessory])−log( Pr [price|keyword 1 ,product])+log( Pr [price|keyword 2 ,accessory])−log( Pr [price|keyword 2 ,product]) . . . log( Pr [price|keyword n ,accessory])−log( Pr [price|keyword n ,product]),   wherein:
 keyword 1 , keyword 2 , through keyword n  are the query keywords; 
 Pr [price|keyword n , product] is a probability that a given result is a product result based on a result price and a product price mean and a product price standard deviation of an n th  keyword in the query; 
 Pr [price|keyword n , accessory] is a probability that a given result is an accessory result based on the result price and an accessory price mean and an accessory price standard deviation of the n th  keyword in the query; and 
   the result is determined to be an accessory result if P TOTAL  is greater than a threshold score.   
     
     
         19 . The system of  claim 18 , further comprising an offer processor operable to determine the keyword price distribution data from the corpus of offer data. 
     
     
         20 . The system of  claim 19 , wherein determining the keyword price distribution data from the corpus of offer data comprises:
 classifying offers in the corpus of offer data as product offers and accessory offers;   determining a product price mean and a product price standard deviation for offer keywords of product offers; and   determining an accessory price mean and an accessory price standard deviation for offer keywords of accessory offers.   
     
     
         21 . The system of  claim 19 , wherein determining the keyword price distribution data from the corpus of offer data comprises:
 heuristically classifying offers in the corpus of offer data as product offers and accessory offers;   determining price distributions for offer keywords corresponding to offers classified as accessory offers;   determining price distributions for offer keywords corresponding to offers classified as product offers; and   reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.   
     
     
         22 . The system of  claim 21 , wherein determining the keyword price distribution data from the corpus of offer data further comprises:
 until a number of reclassified offers are below a threshold number:
 determining price distributions for offer keywords corresponding to offers classified as accessory offers; 
 determining price distributions for offer keywords corresponding to offers classified as product offers; and 
 reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions. 
   
     
     
         23 . The system of  claim 21 , wherein the price distributions are Gaussian distributions. 
     
     
         24 . The system of  claim 14 , wherein classifying the query as a product query or an accessory query comprises:
 comparing query keywords to an accessory blacklist;   classifying the query as an accessory query if query keywords are found in the accessory blacklist.   
     
     
         25 . The system of  claim 14 , wherein classifying the query as a product query or an accessory query comprises:
 if a threshold number of results are classified as accessory offers according to a classification operation, classifying the query as an accessory query.   
     
     
         26 . The system of  claim 25 , further comprising an offer processor operable to perform the classification operation, the previous classification operation comprising:
 heuristically classifying offers in the corpus of offer data as product offers and accessory offers;   determining price distributions for offer keywords corresponding to offers classified as accessory offers;   determining price distributions for offer keywords corresponding to offers classified as product offers;   reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.   
     
     
         27 . A computer implemented method comprising:
 receiving a query from a client device;   classifying the query as a product query or an accessory query;   if the query is classified as a product query:
 identifying a set of ranked results corresponding to the product query from a corpus of offer data, where a rank of a result is selectively modified based on whether the result is classified as a product result or an accessory result, such classification being based at least in part on price distribution of the offer data. 
   
     
     
         28 . The method of  claim 27 , wherein selectively modifying a rank of a result based on whether the result is classified as a product result of an accessory result comprises demoting a rank of a result classified as an accessory. 
     
     
         29 . The method of  claim 27 , wherein selectively modifying a rank of a result based on whether the result is classified as a product result of an accessory result comprises promoting a rank of a result classified as a product. 
     
     
         30 . The method of  claim 27 , wherein:
 a result is classified as a product result or an accessory result according to a likelihood score based on query keywords and keyword price distribution data determined from the corpus of offer data.   
     
     
         31 . The method of  claim 30 , wherein the likelihood score is a log likelihood ratio score of product prices and accessory prices. 
     
     
         32 . The method of  claim 31 , wherein the likelihood ratio score is determined according to:
     P   TOTAL =log( Pr [price|keyword 1 ,accessory])−log( Pr [price|keyword 1 ,product])+log( Pr [price|keyword 2 ,accessory])−log( Pr [price|keyword 2 ,product]) . . . log( PR[price|keyword   n ,accessory])−log( Pr [price|keyword n ,product]),   wherein:
 keyword 1 , keyword 2 , through keyword n , are the query keywords; 
 Pr [price|keyword n , product] is a probability that a given result is a product result based on a result price and a product price mean and product price standard deviation of an n th  keyword in the query; 
 Pr [price|keyword n , accessory] is a probability that a given result is an accessory result based on the result price and an accessory price mean and an accessory price standard deviation of the n th  keyword in the query; and 
   the result is determined to be an accessory result if P TOTAL  is greater than a threshold score.   
     
     
         33 . A computer implemented method comprising:
 classifying offers from a corpus of offer data into a cluster of product offers and a cluster of accessory offers;   computing a price distribution of the cluster of product offers;   computing a price distribution of the cluster of accessory offers;   classifying title word listings associated with the offers based on a likelihood that the offer belongs to a cluster according to the price distributions.   
     
     
         34 . The method of  claim 33 , wherein classifying offers from a corpus of offer data into a cluster of product offers and a cluster of accessory offers comprises:
 heuristically classifying offers in the corpus of offer data as product offers and accessory offers;   determining price distributions for offer keywords corresponding to offers classified as accessory offers;   determining price distributions for offer keywords corresponding to offers classified as product offers; and   reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.   
     
     
         35 . The method of  claim 34 , wherein classifying offers from a corpus of offer data into a cluster of product offers and a cluster of accessory offers further comprises:
 until a number of reclassified offers is below a threshold number:
 determining price distributions for offer keywords corresponding to offers classified as accessory offers; 
 determining price distributions for offer keywords corresponding to offers classified as product offers; and 
 reclassifying the offers based on a determined likelihood that a given offer is a product offer or an accessory offer according to the price of the offer and the determined price distributions.