摘要翻译：
本文利用旧金山湾区数千名匿名手机用户的数据分析了消费者对午餐餐厅的选择。这些数据被用来识别用户大约典型的早上位置，以及他们选择午餐时间的餐馆。我们建立了一个模型，其中餐厅具有潜在特征（其分布可能取决于餐厅可观察到的信息，如星级、食物类别和价格范围），每个用户都对这些潜在特征有偏好，并且这些偏好在用户之间是异构的。类似地，每个项目都有描述用户去餐馆旅游意愿的潜在特征，每个用户对这些潜在特征都有个人特定的偏好。因此，用户的旅行意愿和他们对每个餐馆的基本效用在用户-餐馆对之间都有所不同。我们使用贝叶斯方法进行估计。为了使估计在计算上可行，我们依靠变分推理来逼近后验分布，以及随机梯度下降作为一种计算方法。我们的模型比多项logit和嵌套logit模型等更标准的竞争模型表现得更好，部分原因是估计的个性化。我们分析了当一家餐馆关闭后，消费者如何重新分配他们的需求，与更远的具有相似特征的餐馆相比，我们将我们的预测与实际结果进行了比较。最后，我们展示了如何使用该模型来分析反事实问题，如在给定的位置，什么样的餐馆会吸引最多的消费者。
---
英文标题：
《Estimating Heterogeneous Consumer Preferences for Restaurants and Travel
  Time Using Mobile Location Data》
---
作者：
Susan Athey, David Blei, Robert Donnelly, Francisco Ruiz, and Tobias
  Schmidt
---
最新提交年份：
2018
---
分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
--
一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--
一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

---
英文摘要：
  This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.
---
PDF链接：
https://arxiv.org/pdf/1801.07826