An Interaction Model for Literature Recommendation Based on Cognitive Principle
Xue Chen, Chao Wu, Yinghu Gao · 2012
Current mainstream search engines cannot achieve high accuracy, viz. the users cannot find their desired resources even when clicking on lots of returned links. One of the main reasons is the semantic gap between computers and humans. Computers cannot totally understand human natural language, while humans can hardly understand the binary machine language so that computers may be unable to catch the real search intention. This paper proposes a new model for literature search and recommendation that makes use of the complementary abilities of both cognitive principles and interactions. The goal is to improve the recommendation precision and enable the human-computer interaction to be as smooth as the human-human interaction.