PROPOSING OBJECTS TO A USER TO EFFICIENTLY DISCOVER DEMOGRAPHICS FROM ITEM RATINGS

摘要

The current methods and apparatus provide a system that learns a private attribute, such as gender, based on at least one iteration of presenting an item to a user and receiving ratings from the user for this item. In an exemplary embodiment, the system may solicit ratings for strategically selected items, such as movies for example, and then infers the user's gender. Based on the assessed confidence in the demographic selected, the system may repeat the selection, presentation and ratings of another item. The proposed system can strategically select the sequence of items that are presented to the user for a rating. By selecting the next item to be rated based on a maximum posterior probability confidence, a demographic with a certain threshold of confidence can be inferred. The inventive arrangements are based on novel usage of Bayesian matrix factorization in an active learning setting. Such a system is shown to be feasible and can be carried out using significantly fewer rated items than previously proposed static inference methods.

主权项

1. A method for determining demographic information of a user, comprising:
accessing information in a set; generating a profile matrix by matrix factorization for each of a plurality of items in the set relating to demographic information; selecting an item to present to the user; receiving a rating said user has assigned, if any, to the selected item; finding a solution to a system of linear equations based on the rating from said user and said profile matrix to generate demographic information regarding the user; and, assessing whether a confidence in said demographic information is greater than a threshold, and if not, iteratively repeating said selecting, receiving, finding and assessing said selecting being based on the at least one item having maximum posterior probability.