New collaborative recommendation approach based on concept lattice

The successful design of collaborative filtering system for recommending depends hardly on finding the nearest neighbors. In this paper, we provide a new collaborative filtering method based on concept lattices to generate more precise recommendation. Firstly, we analyze the log files and construct a formal context which is then used to build a concept lattice. Based on the concept lattice, we propose a new way of dynamically recognizing the user´s independent interests and making a Top-N recommendation to suggest what the user may want to visit next. One advantage of our method is that it can quickly group user´s interest based on their navigation history. Besides, a new algorithm based on concept lattice is proposed ground on multi-attribute decision making theory. Further more, it smoothed the cold start problem in traditional collaborative filtering methods. The developed method is validated by the practical web access logs. The experiment shows our approach is practical, feasible and efficient.