METHOD FOR COLD START OF A MULTI-ARMED BANDIT IN A RECOMMENDER SYSTEM

摘要

A method performed by a recommender system to recommend items to a new user includes calculating reward estimates from multiple multi-armed bandit models of a user and her social network friends. The new user's social network friends have multi-armed bandit models that are well established. The mixed multi-armed bandit estimates are processed to select the arm that maximizes the estimated reward to the new user. The multi-armed bandit arm of the greatest reward estimate is played and the new user responds by providing feedback so that the new user's multi-armed bandit model is updated as time progresses.

主权项

1. A method performed by a recommender system to recommend items to a user, the method comprising:
receiving a request to provide a user with a recommendation for an item; calculating reward estimates and selecting a recommendation item for the user, the calculation dependent upon both user reward estimates for recommendation items using a multi-armed bandit model of the user and neighbor reward estimates for recommendation items using a multi-armed bandit model of at least one user neighbor in a social network of the user; sending the selected recommendation item to the user; and receiving feedback from the user concerning the selected recommendation.