%0 Journal Article %A QIAO Yongwei %A XIA Kewen %A XIAO Chunjing %A ZHANG Yuxiang %T Tourism route recommendation based on dynamic clustering %D 2017 %R 10.11772/j.issn.1001-9081.2017.08.2395 %J Journal of Computer Applications %P 2395-2400 %V 37 %N 8 %X In session-based Collaborative Filtering (CF), a user interaction history is divided into sessions using fixed time window and user preference is expressed by sequences of them.But in tourism data, there is no interaction in some sessions and it is difficult to select neighbors because of high sparsity. To alleviate data sparsity and better use the characteristics of the tourism data, a new tourism route recommendation method based on dynamic clustering was proposed. Firstly, the different characteristics of tourism data and other standard data were analyzed. Secondly, a user interaction history was divided into sessions by variable time window using dynamic clustering and user preference model was built by combining probabilistic topic distribution obtained by Latent Dirichlet Allocation (LDA) from each session and time penalty weights. Then, the set of neighbors and candidate routes were obtained through the feature vector of users, which reflected the characteristics of tourist age, route season and price. Finally, routes were recommended according to the relevance of probabilistic topic distribution between candidate routes and tourists. It not only alleviates data sparsity by using variable time window, but also generates the optimal number of time windows which is automatically obtained from data. User feature vector was used instead of similarity of tourism data to select neighbors, so as to the avoid the computational difficulty caused by data sparsity. The experimental results on real tourism data indicate that the proposed method not only adapts to the characteristics of tourism data, but also improves the recommendation accuracy. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2017.08.2395