A Hybrid Recommender Model for Scientific Research Resources
Yi Shen, Jianjun Yu, Kai Nan · 2012
How to find those really useful knowledge from massive information more effectively and more quickly is becoming research focus nowadays. Internet would produce large scale of knowledge which is out of scope of users with the phenomenon of information expansion. Its inconvenient to find those interested information just searching search engine like Google and Baidu with keywords and browse Web pages selecting useful information, people want to get interested knowledge continuously through pushing technology. Recommendation is of great significance in knowledge discovery. Recommender systems typically produce a list of recommendations in one of two ways - through collaborative or content-based filtering. In this paper, we would introduce a hybrid recommendation approach, which has unified content-based recommendation algorithm and item base collaborative filtering recommendation algorithm. We use this model to recommend the Web pages in our own collaborative system, and the experiments showed that our model can make the recommendation results more precisely.