基于增量关联规则游记挖掘的景点推荐应用研究

Application Research on Incremental Association Rules for Travelogue Mining-based Tourist Attractions Recommending

文章提出了一种基于旅游文记挖掘的改进增量关联规则景点推荐算法。该算法紧密关注旅行者偏好,基于分类有效降低了算法空间复杂度,使得挖掘结果聚焦度更高;能够高效处理旅游文记数量增长的状况,避免了反复扫描整个数据集,仅需扫描增量数据集并结合已有挖掘结果便可开展高效运算分析,完成相关景点推荐应用。通过使用网络获取旅游文记作为实例对算法进行验证,表明改进算法在候选项集获取个数方面减少明显,推荐结果清晰明了,有较明显的优势。

As the travelers would like the system to automatically recommend relevant scenery spots to optimize their travel plan based on their preferences and selections,an improved incremental updating algorithm of association rules based on tourist attraction recommendation is proposed. The algorithm focuses on the traveler＇s preferences and its classification effectively reduces the space complexity of the algorithm,making the mining result more concentrated. It can efficiently handle the growth of travel documents,avoiding scanning the entire data set and only scanning the incremental data set and combining the results with the existing mining results.Through the experimental verification,the improved algorithm can significantly reduce the number of candidates,and the results are clear and have obvious advantages.