|
カワイ ユキコ
KAWAI YUKIKO
河合 由起子 所属 京都産業大学 情報理工学部 情報理工学科 職種 教授 |
|
| 発行・発表の年月 | 2023 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | User Latent Interest Estimation in Real Space: A Comparative Analysis of Time-Series and Non-Time-Series Processing Algorithms |
| 執筆形態 | その他 |
| 掲載誌名 | Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023 |
| 巻・号・頁 | 2131-2138頁 |
| 著者・共著者 | Takanobu Omura,Da Li,Panote Siriaraya,Katsumi Tanaka,Yukiko Kawai,Shinsuke Nakajima |
| 概要 | Web advertising services have exhibited consistent growth over the years. However, the conventional methods of web advertising recommendations, relying on keyword matching with search queries and browsing histories, encounter challenges when it comes to effectively targeting users with hidden or latent interests. In contrast, the use of mobile device location data in advertising recommendations often centers around physical store proximity. To address these limitations, our research aims to enhance web advertising recommendations by analyzing latent user interests through real-world behavioral data. This study specifically investigates the influence of area size on user behavioral analysis and its subsequent impact on the accuracy of predicting visit probabilities. We achieve this by extracting the user's activity range from user behavior (movement) log data and geotagged tweets. Subsequently, we tally the places visited by the user, considering spot attributes, and convert this data into feature vectors. Utilizing these feature vectors in conjunction with various classification methods, we build learning models. In this paper, we present and evaluate these learning models employing different area sizes, verifying their accuracy in predicting user visits to specific stores. |
| DOI | 10.1109/BigData59044.2023.10386563 |
| DBLP ID | conf/bigdataconf/OmuraLSTKN23 |
| PermalinkURL | https://dblp.uni-trier.de/rec/conf/bigdataconf/2023 |
| researchmap用URL | https://dblp.uni-trier.de/db/conf/bigdataconf/bigdataconf2023.html#OmuraLSTKN23 |