[Objective] This paper tries to address the challenges facing Smart Tourism industry, such as data sparseness and cold start, with the help of collaborative recommendation technology. [Methods] First, we clustered users with the K-means algorithm and then filtered and classified them dynamically based on the combination of collaborative recommendation technology. Then, we assigned weight to the recommended types and proposed a new algorithm based on Improved Uncertain Neighbors Collaborative Filtering (IUNCF). Finally, we examined the proposed algorithm with real world tourism data of different similarity thresholds and recommended numbers. [Results] The MAE value and F-measure reached 0.243 and 0.764, which showed the effectiveness of IUNCF in accuracy and reliability. [Limitations] The IUNCF algorithm needs to be further optimized to deal with the low frequency consumption issue. We could also extend the application of this new model. [Conclusions] The proposed IUNCF algorithm could precisely recommend smart tourism products to the consumers.
赵雅楠, 王育清. 基于不确定近邻的旅游产品协同过滤推荐算法研究*[J]. 数据分析与知识发现, 2018, 2(7): 63-71.
Zhao Ya’nan,Wang Yuqing. Research on Collaborative Filtering Traveling Products Recommendation Algorithm Based on IUNCF. Data Analysis and Knowledge Discovery, 2018, 2(7): 63-71.
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