Prediction of user interest based on collaborative filtering for personalized academic recommendation
Jie Yu, Kege Xie, Haihong Zhao, Fangfang Liu · 2012
The development of Internet provides academic researchers with abundant information, which also brings them heavier and heavier information burden, because to obtain what they exactly want from the huge amount of resources will greatly affects the efficiency of information seeking. To alleviate user's information burden, academic recommendation which aims at automatically providing articles to researcher according to their interests has caught much attention. In academic recommendation, how to accurately predict user interest is a key issue. This paper presents a prediction method based on collaborative filtering. First, it is implemented based on refined user profile in which concept weights is adjusted. In the adjustment, the continuity feature of user's browsing content is taken into account, which is helpful in discovering collaborative users. Secondly, key concepts are selected from refined user profile. And collaborative users are discovered based only on key concepts, which can improve the efficiency of prediction. Thirdly, when extracting concepts that can represent user future interest, information quantity is presented as the evaluation attribute. In addition, semantic relations between concepts are considered when computing information quantity, which can ensure the accuracy of prediction. Experimental results demonstrate the validity and effectiveness of this method.