CoSTUR: 面向用户评级的空间文本竞争选址
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陕西省自然科学基础研究计划(2023-JC-YB-558)


CoSTUR: Competitive Spatio-textual Location Selection Based on User Rating
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    摘要:

    随着GPS定位技术和移动互联网的发展, 各类LBS (location-based service)应用积累了大量带有位置和文本标记的空间文本数据, 这些数据广泛应用于市场营销、城市规划等设施选址决策中. 空间文本选址的目标是从候选位置集合中挖掘最佳地点新建设施, 以期影响最多空间文本对象, 如用户或车辆等, 其中空间距离越接近且文本越相似则影响力越大. 现有方案未考虑现实普遍存在的同行竞争, 也忽略了用户对设施的评价因素. 为更合理地在同行竞争环境结合用户评级进行选址决策, 本文提出新的空间文本竞争选址问题CoSTUR. 通过引入权衡影响的确定性和数量的阈值, 解决传统模型中对象只能被单一设施影响的局限, 建模了用户可能同时受多个设施影响的真实情况. 借鉴经典的竞争均分模型, 实现了不同评级设施间竞争量化. 为降低大规模数据导致的高昂计算代价, 构建了新型空间文本索引结构TaR-tree, 并结合阈值设计基于影响范围的两个剪枝策略, 实现基于分支定界思想的空间连接和范围查询两种方案. 在真实和合成数据集上的实验结果显示, 相比基线算法计算效率能够提升近一个量级, 说明提出方法的有效性.

    Abstract:

    With the development of GPS positioning technology and mobile Internet, various location-based services (LBS) applications have accumulated a large amount of spatio-textual data with location and text markup. These data are widely used in location selection decision-making scenarios such as marketing and urban planning. The goal of spatio-textual location selection is to mine the optimal locations from a given candidate set to build new facilities to influence the largest number of spatio-textual objects, such as people or vehicles, where the closer the spatial location and the more similar the text, the greater the influence. However, existing solutions not only fail to consider prevalent peer competition in real life but also ignore user evaluation factors for facilities. To make more reasonable location selection decisions in a peer competition environment combined with user ratings, this study proposes a more rational spatio-textual location selection problem, CoSTUR. To solve the limitation in traditional models where objects can only be influenced by a single facility, a threshold that makes a trade-off between the certainty and quantity of facility influence on objects is introduced, which also models the real-world situation in which multiple facilities could simultaneously influence a specific user. Based on the classical competitive equalization model, quantification of competition among facilities with different ratings is achieved. To reduce the high computational cost for large volumes of data, a novel spatio-textual index structure, TaR-tree, is constructed and two pruning strategies based on influence range are designed with a combination of thresholds to achieve two branch-and-bound solutions for spatial connectivity and range queries. Experimental results on real and synthetic datasets demonstrate that the computational efficiency can be improved by nearly one order of magnitude compared to baseline algorithms, verifying the effectiveness of the proposed method.

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李晨伟,默梓鹏,赵梦霏. CoSTUR: 面向用户评级的空间文本竞争选址.计算机系统应用,,():1-11

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  • 收稿日期:2024-02-17
  • 最后修改日期:2024-03-19
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  • 在线发布日期: 2024-07-03
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