Abstract
Understanding user's information needs is the premise and basis to improve the quality of information services and systems. When engaging in various activities, people have run into problems that are followed by the realization of insufficient knowledge structure to resolve these problems, and the need for information has arisen. After information needs are generated, they have experienced an expression process from the internal to the external and from the abstract to the concrete. The process of information need expression is influenced by the users themselves and their surrounding environment. Ambiguities will appear during this process. The current systems mainly promote understanding through (1) information extraction and knowledge acquisition, (2) ontology, semantic web, and knowledge graphs, (3) semantic disambiguation, and (4) topic modeling. This research proposes a new promotion mechanism for the understanding of user information needs. Based on collaborative filtering and folksonomy, the mechanism can precisely match the user with other most related users and get the user's knowledge structure.
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