Simulation of Book Recommender System Based on Eclat Algorithm
Kang Chen · Jisuanji fangzhen · 2010
In the research on library's personal recommender system,collaborative filtering is the common method in recommender system,but it cannot handle large data efficiently. An improved algorithm based on Eclat is given in the paper. The new algorithm is applied in simulation experiments of book recommendation system. The new algorithm makes use of a vertical data representation and cross-count high-performance advantage,generates frequent patterns directly in the vertical data representation of the data set through the breadth-first search and cross-count. Association rules are generated from library database. The simulation results show that the algorithm can achieve an efficient association rule mining on large data,and the knowledge generated by the new algorithm is effective for book recommendation.