Adaptive pairwise preferences in recommenders

Abstract

Methods, systems, and products adapt recommender systems with pairwise feedback. A pairwise question is posed to a user. A response is received that selects a preference for a pair of items in the pairwise question. A latent factor model is adapted to incorporate the response, and an item is recommended to the user based on the response.

1 . (canceled) 
     
     
         2 . A method, comprising:
 sending a sequence of questions from a server to a client device, each question in the sequence of questions soliciting one content item from a pair of different content items;   receiving at the server successive responses from the client device to the sequence of questions, each successive response selecting a preference for the one content item in the pair of different content items in each question;   sending the successive responses as feedback to a latent factor model for recommending content to the client device; and   receiving, after each successive response, a probability of a preference for another content item in another pair of different content items.   
     
     
         3 . The method of  claim 2 , comprising incorporating adaptive pairwise preference feedback into the latent factor model, such that previous feedback on pairs of different content items affects future pairs of different content items. 
     
     
         4 . The method of  claim 3 , wherein the latent factor model operates in a Bayesian framework. 
     
     
         5 . The method of  claim 2 , comprising generating a recommendation for content based on the successive responses. 
     
     
         6 . The method of  claim 2 , comprising querying for one question in the sequence of questions. 
     
     
         7 . The method of  claim 2 , comprising predicting a difference in the preference for the pair of different content items. 
     
     
         8 . The method of  claim 2 , comprising determining a change in entropy after each successive response. 
     
     
         9 . A system, comprising:
 a processor; and   memory storing code that when executed causes the processor to perform operations, the operations comprising:   sending a sequence of questions from a server to a client device, each question in the sequence of questions soliciting one content item from a pair of different content items;   receiving at the server successive responses from the client device to the sequence of questions, each successive response selecting a preference for the one content item in the pair of different content items in each question;   sending the successive responses as feedback to a latent factor model for recommending content to the client device; and   receiving, after each successive response, a probability of a preference for another content item in another pair of different content items.   
     
     
         10 . The system of  claim 9 , comprising incorporating adaptive pairwise preference feedback into the latent factor model, such that previous feedback on the pairs of different content items affects future pairs of different content items. 
     
     
         11 . The system of  claim 10 , wherein the latent factor model operates in a Bayesian framework. 
     
     
         12 . The system of  claim 9 , wherein the operations comprise generating a recommendation for content based on the successive responses. 
     
     
         13 . The system of  claim 9 , wherein the operations comprise querying for one question in the sequence of questions. 
     
     
         14 . The system of  claim 9 , wherein the operations comprise predicting a difference in the preference for the pair of different content items. 
     
     
         15 . The system of  claim 9 , wherein the operations comprise determining a change in entropy after each successive response. 
     
     
         16 . A memory storing instructions that when executed cause a processor to perform operations, the operations comprising:
 sending a sequence of questions from a server to a client device, each question in the sequence of questions soliciting one content item from a pair of different content items;   receiving at the server successive responses from the client device to the sequence of questions, each successive response selecting a preference for the one content item in the pair of different content items in each question;   sending the successive responses as feedback to a latent factor model for recommending content to a user of the client device; and   receiving, after each successive response, a probability of a preference for another content item in another pair of different content items.   
     
     
         17 . The memory of  claim 16 , comprising incorporating adaptive pairwise preference feedback into the latent factor model, such that previous feedback on the pairs of different content items affects future pairs of different content items. 
     
     
         18 . The memory of  claim 17 , wherein the latent factor model operates in a Bayesian framework. 
     
     
         19 . The memory of  claim 16 , wherein the operations comprise generating a recommendation for content based on the successive responses. 
     
     
         20 . The memory of  claim 16 , wherein the operations comprise querying for one question in the sequence of questions. 
     
     
         21 . The memory of  claim 16 , wherein the operations comprise predicting a difference in the preference for the pair of different content items.