邵长城 陈平华

摘 要：基于位置的社交网络（LBSN）蓬勃发展，带来了大量的兴趣点（POI）数据，加速了兴趣点推荐的研究。针对用户兴趣点矩阵极端稀疏造成的推荐精度低和兴趣点特征缺失问题，通过融合兴趣点的标签、地理、社交、评分以及图像等信息，提出了一种融合社交网络和图像内容的兴趣点推荐方法（SVPOI）。首先分析兴趣点数据集，针对地理信息，利用幂律概率分布构造距离因子;针对标签信息，利用检索词频率构造标签因子;融合已有的历史评分数据，构造新的用户兴趣点评分矩阵。其次利用VGG16深度卷积神经网络模型（DCNN）识别兴趣点图像内容，构造兴趣点图像内容矩阵。然后根据兴趣点数据的社交网络信息，构造用户社交矩阵。最后，利用概率矩阵分解（PMF）模型，融合用户兴趣点评分矩阵、图像内容矩阵、用户社交矩阵，构成SVPOI兴趣点推荐模型，生成兴趣点推荐列表。大量的真实数据集上的实验结果表明，与PMF、SoRec、TrustMF、TrustSVD推荐算法相比，SVPOI推荐的准确度均有较大提升，其平均绝对误差（MAE）和均方根误差（RMSE）两项指标比最优的TrustMF算法分别降低了5.5%和7.82%，表明SVPOI具有更好的推荐效果。

关键词：兴趣点推荐;基于位置的社交网络;图像内容;深度卷积神经网络;概率矩阵分解模型

中图分类号：TP18

文献标志码：A

Abstract： The rapid growth of LocationBased Social Networks （LBSN） provides a vast amount of PointofInterest （POI） data， which facilitates the research of POI recommendation. To solve the low recommendation accuracy caused by the extreme sparseness of userPOI matrix and the lack of POI features， by integrating information such as tags， geography， socialization， score， and image information of POI， a POI recommendation method integrating social networks and image contents called SVPOI was proposed. Firstly， with the analysis of POI dataset， a distance factor was constructed based on power law distribution and a tag factor was constructed based on term frequency， and the existing historical score data was merged to construct a new userPOI matrix. Secondly， VGG16 Deep Convolutional Neural Network （DCNN） was used to process the images of POI to construct the POI image content matrix. Thirdly， the user social matrix was constructed according to the social network information of POI data. Finally， with the use of Probabilistic Matrix Factorization （PMF） model， the POI recommendation list was obtained with the integration of userPOI matrix， image content matrix and user social matrix. On realworld datasets， the accuracy of SVPOI is improved significantly compared to PMF， SoRec （Social Recommendation using probabilistic matrix factorization）， TrustMF （Social Collaborative Filtering by Trust） and TrustSVD （Social Collaborative Filtering by Trust with SVD） while Mean Absolute Error （MAE） and RootMeanSquare Error （RMSE） of SVPOI are decreased by 5.5% and 7.82% respectively compared to those of TrustMF which was the best of the comparison methods. The experimental results demonstrate the recommendation effectiveness of the proposed method.

英文關键词Key words： pointofinterest recommendation; LocationBased Social Network （LBSN）; image content; Deep Convolutional Neural Network （DCNN）; Probabilistic Matrix Factorization （PMF） model

可见基于矩阵分解的推荐模型可以灵活扩展，成为研究人员构造个性化推荐模型的重要模型， 所以，对于兴趣点的推荐，依然可以沿用这一基础模型进行不断扩展。兴趣点不同于物品推荐，因为兴趣点不仅仅是地理上的点，更具有很多抽象的意义。……