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Design of Large Data Evaluation Model for Optimal Tourist Attractions

机译:最优旅游景点大数据评价模型设计

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It is of great importance to recommend the optimal tourist attractions through big data analysis. Therefore, in this paper we introduce the LDA topic model to recommend tourist attractions for travelers. The LDA model refers to a three layer hierarchical Bayesian model, in which each element of a collection is represented as a finite mixture on underlying topics. Afterwards, the weight of user vector is calculated by a TF-IDF policy where the TF is word frequency in user's profile and IDF is the number of users who have focused on a particular tourist attraction. Furthermore, a user is represented by a vector, in which each dimension is a latent topic of LDA. Next, the proposed personalized tourist attraction recommendation algorithm is given. Experimental results demonstrate that the proposed can effectively find optimal tourist attractions for users.
机译:通过大数据分析推荐最佳的旅游景点非常重要。因此,在本文中,我们介绍了LDA主题模型,以为旅行者推荐旅游景点。 LDA模型是指三层层次的贝叶斯模型,其中集合的每个元素都表示为基础主题的有限混合。然后,通过TF-IDF策略计算用户矢量的权重,其中TF是用户个人资料中的字频,IDF是关注特定旅游景点的用户数量。此外,用向量表示用户,其中每个维度都是LDA的潜在主题。接下来,给出了所提出的个性化旅游景点推荐算法。实验结果表明,该方法可以有效地为用户找到最佳的旅游景点。

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