Verification of a method for latent interest estimation based on user behavior analysis and POI attributes
Verification of a method for latent interest estimation based on user behavior analysis and POI attributes
复制标题
DOI：
10.1109/icnc57223.2023.10074037
复制
发表时间：
2023-02
期刊：
影响因子：
--
通讯作者：
Takanobu Omura;Felix Dollack;Panote Siriaraya;Da Li;Katsumi Tanaka;Yukiko Kawai;Shinsuke Nakajima-Shinsuke-Nakajim
中科院分区：
文献类型：
--
作者：
Takanobu Omura;Felix Dollack;Panote Siriaraya;Da Li;Katsumi Tanaka;Yukiko Kawai;Shinsuke Nakajima-Shinsuke-Nakajim
互联网的网络广告服务正在迅速增长。然而，使用当前主流的基于关键字与在线搜索和浏览器历史匹配的Web广告推荐方法很难有效地向潜在买家推荐Web广告。这是因为它是一个明确的分析，使用用户已经感兴趣的关键字。另一方面，大多数使用移动的终端的位置信息的广告推荐方法是基于到实际商店的距离。因此，本研究提出并验证了一种方法来分析潜在的用户兴趣的基础上，考虑到兴趣点（POI）属性的真实空间用户行为的分析。我们将该方法应用于移动的终端上的Web广告推荐，并假设用户的兴趣目标是存在于真实的空间中的商店。具体来说，我们使用地理标记的鸣叫数据提取用户的运动活动范围在一定的时间段内，参考商店的位置。运动活动范围用作使用OpenStreetMap（OSM）数据进行POI属性分析的基础。最后，提取真实空间运动活动历史的特征，并与POI属性一起用于训练XGBoost模型。在之前的研究中，我们积累了数据，生成了各种设置的训练模型，并验证了它们对访问目标商店的用户的预测准确性。本文通过对实际用户进行问卷调查，重点验证了所提出的广告推荐方法的有效性。
Web advertising services for the Internet are growing rapidly. However, it is difficult to effectively recommend Web advertisements to latent buyers using the current mainstream Web ad recommendation method based on keyword matching with online search and browser history. This is because it is an explicit analysis using keywords that the user is already interested in. On the other hand, most of the advertisement recommendation methods using location information of mobile terminals are based on the distance to the actual store. Therefore, this research proposes and verifies a method to analyse latent user interest based on analysis of real-space user behavior considering point-of-interest (POI) attributes. We apply the method to Web advertisement recommendation on mobile terminals, and assume that the user’s interest targets are stores that exist in real space. Specifically, we use geotagged tweet data to extract the users’ movement activity range within a certain time period with reference to the store locations. The movement activity range serves as basis for POI attribute analysis using OpenStreetMap (OSM) data. Finally, the characteristics of the real-space movement activity history are extracted and used together with the POI attributes to train a XGBoost model. In previous research, we accumulated data, generated training models with various settings, and verified their prediction accuracy of users visiting a target stores. In this paper, we focus on verifying the effectiveness of the proposed advertising recommendation method by conducting a questionnaire survey of actual users.