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.
Please help me write a proper abstract based on the patent claims. CLAIM: 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.