Item-based Non-neighbouring Sequential Pattern Recommendation Algorithms
Ni Liu · Jisuanji gongcheng · 2009
To solve the sparsity problems of recommender systems,a recommendation method is designed by means of the combination of the non-neighbouring sequential pattern mining algorithms and the item-based Collaborative Filtering(CF) recommendation algorithms.By constructing the Markov probability transfer matrix according to the paths weight sum algorithms,it computes the recommendation possibility of resources and recommends to users.Experimental results show that,on the condition of sparse data,it can improve the recommendation quality compared with the traditional item-based Collaborative Filtering recommendation algorithms.