A user has an inherent predisposition to have an interest for a particular item. The user's interests may also be affected by what people in her social circle are interested in. To more accurately make recommendations, a user's inherent interests, social influence, how a user responds to recommendations, and/or the user's desire for novelty are taken into consideration. Considering the evolution of users' interests in response to the users' social interactions and users' interactions with the recommender system, the recommendation problem is formulated as an optimization problem to maximize the overall expected utilities of the recommender system. Tractable solutions to the optimization problem are presented for some use cases: (1) when the system does not perform personalization; (2) when the users in the system exhibit attraction dominant behavior; and (3) when the users in the system exhibit aversion dominant behavior.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for providing recommendations to a user, comprising: analyzing the user's response to recommendation service to determine a level of acceptance and desire for novelty with respect to previous recommendations; determining an updated interest profile of the user based on the user's response to the recommendation service; and recommending an item to the user based on the updated user's interest profile. 2. The method of claim 1, wherein the user's response to the recommendation service includes at least one of: a. accepting a recommendation provided at a previous time step, b. accepting an average of the previous recommendations, c. accepting a recommendation that is different from what is provided at a previous time step, and d. accepting a recommendation that is different from an average of the previous recommendations. 3. The method of claim 2, further comprising: determining a probability at which a user accepts the recommendation generated at the previous time step or the average of the previous recommendations. 4. The method of claim 1, further comprising: determining a probability at which the user is influenced by the user's social circle, wherein the determining the updated interest profile is further based on the influence by the user's social circle. 5. The method of claim 4, wherein the user is not influenced by the user's social circle at another probability. 6. The method of claim 4, further comprising: determining a probability at which the user adopts an interest profile of another user in the user's social circle. 7. The method of claim 1, the recommendation service recommending items to a plurality of users further based on inherent user interests, wherein updated interest profiles for the plurality of users are determined to be: Ū−(I−BP)−1AŪ0+(I−BP)−1Γ V−(I−BP)−1Δ V, wherein A, B, Γ, Δ are diagonal matrices whose diagonal elements are coefficients αi, βi, γi, and δi, respectively, P is a matrix whose elements are probabilities Pij, and Ū, Ū0, and V are matrices whose rows comprise expected profiles ūi, ūi0, vi, respectively, αi being a probability that user i follows the inherent user interest of user i, βi being a probability that user i is influenced by social circle of user i, γi being a probability that user i is attracted to the recommendation service, and δi being a probability that user i is averse to the recommendation service, Pij being a probability that user i adopts interest profile of user j, ūi being an expected profile of user i, ūi0 being inherent profile distributions of user i, and vi being an expected profile of an item in a steady state that is recommended to user i. 8. The method of claim 7, wherein the recommended items maximize a function: G( V)≡trace((I−BP)−1AŪ0 VT)+trace( VT(I−BP)−1(Γ−Δ) V). 9. The method of claim 1, the recommendation service recommending items to a plurality of users, further comprising: determining whether the recommendation service recommends a same item to the plurality of users. 10. The method of claim 1, the recommendation service recommending items to a plurality of users, further comprising: determining whether attraction to the recommended service is more dominant than aversion to the recommended service for the plurality of users. 11. An apparatus for providing recommendations to a user, comprising: a recommendation suggestion analyzer configured to analyze the user's response to recommendation service to determine a level of acceptance and desire for novelty with respect to previous recommendations; and a recommendation generator configured to determine an updated interest profile of the user based on the user's response to the recommendation service, and recommend an item to the user based on the updated user's interest profile. 12. The apparatus of claim 11, wherein the user's response to the recommendation service includes at least one of: a. accepting a recommendation provided at a previous time step, b. accepting an average of the previous recommendations, c. accepting a recommendation that is different from what is provided at a previous time step, and d. accepting a recommendation that is different from an average of the previous recommendations. 13. The apparatus of claim 12, wherein the recommendation suggestion analyzer determines a probability at which a user accepts the recommendation generated at the previous time step or the average of the previous recommendations. 14. The apparatus of claim 11, further comprising: a social influence analyzer configured to determine a probability at which the user is influenced by the user's social circle, wherein the recommendation generator determines the updated interest profile further responsive to the influence by the user's social circle. 15. The apparatus of claim 14, wherein the user is not influenced by the user's social circle at another probability. 16. The apparatus of claim 14, wherein the social influence analyzer determines a probability at which the user adopts an interest profile of another user in the user's social circle. 17. The apparatus of claim 11, the recommendation service recommending items to a plurality of users further based on inherent user interests, wherein updated interest profiles for the plurality of users are determined to be: Ū=(I−BP)−1AŪ0+(I−BP)−1Γ V−(I−BP)−1Δ V, wherein A, B, Γ, Δ are diagonal matrices whose diagonal elements are coefficients αi, βi, γi, and δi, respectively, P is a matrix whose elements are probabilities Pij, and Ū, Ū0, and V are matrices whose rows comprise expected profiles ūi, ūi0, vi, respectively, αi being a probability that user i follows the inherent user interest of user i, βi being a probability that user i is influenced by social circle of user i, γi being a probability that user i is attracted to the recommendation service, and δi being a probability that user i is averse to the recommendation service, Pij being a probability that user i adopts interest profile of user j, ūi being an expected profile of user i, ūi0 being inherent profile distributions of user i, and vi being an expected profile of an item in a steady state that is recommended to user i. 18. The apparatus of claim 17, wherein the recommended items maximize a function: G( V)≡trace((I−BP)−1AŪ0 VT)+trace( VT(I−BP)−1(Γ−Δ) V). 19. The apparatus of claim 11, wherein the recommendation generator determines whether the recommendation service recommends a same item to a plurality of users. 20. The apparatus of claim 11, wherein the recommendation generator determines whether attraction to the recommended service are more dominant than aversion to the recommended service for a plurality of users. 21. (canceled)