Beyond Query: Interactive User Intention Understanding

Users often fail to find the right keywords to precisely describe their queries in the information seeking process. Techniques such as user intention predictions and personalized recommendations are designed to help the users figure out how to formalize their queries. In this work, we aim to help users identify their search targets using a new approach called Interactive User Intention Understanding. In particular, we construct an automatic questioner that generates yes-or-no questions for the user. Then we infer user intention according to the corresponding answers. In order to generate "smart" questions in an optimal sequence, we propose the IHS algorithm based on heuristic search. We prove an error bound for the proposed algorithm on the ranking of target items given the questions and answers. We conduct experiments on three datasets and compare our result with two baseline methods. Experimental results show that IHS outperforms the baseline methods by 27.83% and 25.98% respectively.