Geo-SAGE：一个面向空间项目推荐的地理稀疏可加生成模型
Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation
摘要：
随着基于位置的社交网络（LBSNs）的快速发展，空间项目推荐已成为帮助人们发现有吸引力和有趣的场所和活动的重要手段，特别是当用户外出旅游时。然而，与传统的推荐系统相比，这种推荐是非常具有挑战性的。一个用户只能访问有限数量的空间项，导致一个非常稀疏的用户-项矩阵。用户访问的大多数项目位于离他/她居住的地方很短的距离内，这使得当用户旅行到很远的地方时很难推荐项目。此外，不同地理区域的用户兴趣和行为模式可能会有很大差异。鉴于此，我们提出了地理SAGE，一个地理稀疏的空间项目推荐的可加性生成模型。Geo-SAGE通过利用空间项目的共现模式和空间项目的内容，同时考虑用户的个人兴趣和目标区域人群的偏好。为了进一步缓解数据稀疏性问题，Geo-SAGE利用地理相关性，通过一个精心设计的空间索引结构（称为空间金字塔）来平滑人群的偏好。我们进行了广泛的实验，以评估我们的Geo-SAGE模型在两个真正的大规模数据集上的性能。实验结果清楚地表明，我们的Geo-SAGE模型优于国家的最先进的两个任务的外地和家乡的建议。
英文摘要：
With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recommendation is very challenging compared to the traditional recommender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and behavior patterns may vary dramatically across different geographical regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments to evaluate the performance of our Geo-SAGE model on two real large-scale datasets. The experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art in the two tasks of both out-of-town and home-town recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
