  Rating elicitation is a success element for recommender systems to perform
well at cold-starting, in which the systems need to recommend items to a newly
arrived user with no prior knowledge about the user's preference. Existing
elicitation methods employ a fixed set of items to learn the user's preference
and then infer the users' preferences on the remaining items. Using a fixed
seed set can limit the performance of the recommendation system since the seed
set is unlikely optimal for all new users with potentially diverse preferences.
This paper addresses this challenge using a 2-phase, personalized elicitation
scheme. First, the elicitation scheme asks users to rate a small set of popular
items in a ``burn-in'' phase. Second, it sequentially asks the user to rate
adaptive items to refine the preference and the user's representation.
Throughout the process, the system represents the user's embedding value not by
a point estimate but by a region estimate. The value of information obtained by
asking the user's rating on an item is quantified by the distance from the
region center embedding space that contains with high confidence the true
embedding value of the user. Finally, the recommendations are successively
generated by considering the preference region of the user. We show that each
subproblem in the elicitation scheme can be efficiently implemented. Further,
we empirically demonstrate the effectiveness of the proposed method against
existing rating-elicitation methods on several prominent datasets.
