图 书 馆
GeoMF：用于兴趣点推荐的联合地理建模和矩阵分解GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation |
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| 课程网址: | http://videolectures.net/kdd2014_xie_geomf/ |
| 主讲教师: | Xing Xie |
| 开课单位: | 微软亚洲研究院 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  兴趣点（POI）推荐已成为帮助人们发现有吸引力的地点的重要手段。但是，用户POI矩阵的极度稀疏性提出了严峻的挑战。为了应对这一挑战，将基于位置的社交网络（LBSN）上的移动性记录视为POI建议的隐式反馈，我们首先提出针对此任务利用加权矩阵分解，因为它通常可以更好地与隐式反馈一起提供协同过滤功能。此外，研究人员最近在LBSN上发现了人类移动行为中的空间聚类现象，即单个访问位置倾向于聚在一起，并且还证明了其在POI推荐中的有效性，因此我们将其纳入分解模型中。特别是，我们在分解模型中分别使用用户的活动区域向量和POI的影响区域向量来增加用户和POI的潜在因子。基于这样的增强模型，我们不仅可以通过二维核密度估计来捕获空间聚类现象，而且还可以解释为什么将这种现象引入矩阵分解中有助于解决矩阵稀疏性带来的挑战。然后，我们在大型LBSN数据集上评估提出的算法。结果表明，加权矩阵分解优于其他形式的分解模型，将空间聚类现象纳入矩阵分解可以提高推荐性能。 |
| 课程简介: | Point-of-Interest (POI) recommendation has become an important means to help people discover attractive locations. However, extreme sparsity of user-POI matrices creates a severe challenge. To cope with this challenge, viewing mobility records on location-based social networks (LBSNs) as implicit feedback for POI recommendation, we first propose to exploit weighted matrix factorization for this task since it usually serves collaborative filtering with implicit feedback better. Besides, researchers have recently discovered a spatial clustering phenomenon in human mobility behavior on the LBSNs, i.e., individual visiting locations tend to cluster together, and also demonstrated its effectiveness in POI recommendation, thus we incorporate it into the factorization model. Particularly, we augment users' and POIs' latent factors in the factorization model with activity area vectors of users and influence area vectors of POIs, respectively. Based on such an augmented model, we not only capture the spatial clustering phenomenon in terms of two-dimensional kernel density estimation, but we also explain why the introduction of such a phenomenon into matrix factorization helps to deal with the challenge from matrix sparsity. We then evaluate the proposed algorithm on a large-scale LBSN dataset. The results indicate that weighted matrix factorization is superior to other forms of factorization models and that incorporating the spatial clustering phenomenon into matrix factorization improves recommendation performance. |
| 关 键 词: | 协同过滤功能; 兴趣点推荐; 加权矩阵分解; 空间聚类现象 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-27：zyk |
| 最后编审: | 2021-05-27：zyk |
| 阅读次数: | 145 |