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A Recommendation System Combining LDA and Collaborative Filtering Method for Scenic Spot

机译：LDA与协同过滤相结合的景区推荐系统

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摘要

Researchers have long sought to find an effective and straightforward method to bridge the gap between us and big data. Especially during the big data era, how to find the needed information with rapid speed and exact result has become the central concerns of the internet users. This paper focuses on exploring the valuable data in UGC (User Generated Content), and recommending useful information to specified users. To achieve this goal, we model the social network, and then the LDA (Linear Discriminant Analysis), PCA (Principal Component Analysis) and KNN (K-Nearest Neighbour) algorithms are adopted to calculate the recommendation items. Our algorithm avoids the disadvantages of the common collaborative filtering algorithm that only behaviors is considered but without considering the behaviour results, thus our method effectively improves the accuracy of the recommendation system. Experimental results show that our algorithm improves the accuracy comparing with the CF algorithms.
机译：长期以来，研究人员一直在寻找一种有效而直接的方法来弥合我们与大数据之间的鸿沟。特别是在大数据时代，如何快速，准确地找到所需的信息已成为互联网用户关注的焦点。本文着重探讨UGC（用户生成的内容）中的宝贵数据，并向特定用户推荐有用的信息。为了实现此目标，我们对社交网络进行建模，然后采用LDA（线性判别分析），PCA（主成分分析）和KNN（K最近邻）算法来计算推荐项。该算法避免了普通协同过滤算法只考虑行为而不考虑行为结果的弊端，有效地提高了推荐系统的准确性。实验结果表明，与CF算法相比，我们的算法提高了精度。

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