Recently, learning urban region representations utilizing multi-modal data
(information views) has become increasingly popular, for deep understanding of
the distributions of various socioeconomic features in cities. However,
previous methods usually blend multi-view information in a posteriors stage,
falling short in learning coherent and consistent representations across
different views. In this paper, we form a new pipeline to learn consistent
representations across varying views, and propose the multi-view Contrastive
Prediction model for urban Region embedding (ReCP), which leverages the
multiple information views from point-of-interest (POI) and human mobility
data. Specifically, ReCP comprises two major modules, namely an intra-view
learning module utilizing contrastive learning and feature reconstruction to
capture the unique information from each single view, and inter-view learning
module that perceives the consistency between the two views using a contrastive
prediction learning scheme. We conduct thorough experiments on two downstream
tasks to assess the proposed model, i.e., land use clustering and region
popularity prediction. The experimental results demonstrate that our model
outperforms state-of-the-art baseline methods significantly in urban region
representation learning.

本文提出了一种利用多模态数据学习城市区域表示的多视图对比预测模型（ReCP），该模型利用兴趣点和人类流动数据中的多个信息视图，通过内视图学习模块和相互视图学习模块实现对城市区域的一致性和连贯性表示，实验证明该模型在城市区域表征学习中明显优于现有基线方法。

基于多视角对比预测的城市区域嵌入

Urban Region Embedding via Multi-View Contrastive Prediction

The availability of charging stations is an important factor for promoting
electric vehicles (EVs) as a carbon-friendly way of transportation. Hence, for
city planners, the crucial question is where to place charging stations so that
they reach a large utilization. Here, we hypothesize that the utilization of EV
charging stations is driven by the proximity to points-of-interest (POIs), as
EV owners have a certain limited willingness to walk between charging stations
and POIs. To address our research question, we propose the use of web mining:
we characterize the influence of different POIs from OpenStreetMap on the
utilization of charging stations. For this, we present a tailored interpretable
model that takes into account the full spatial distributions of both the POIs
and the charging stations. This allows us then to estimate the distance and
magnitude of the influence of different POI types. We evaluate our model with
data from approx. 300 charging stations and 4,000 POIs in Amsterdam,
Netherlands. Our model achieves a superior performance over state-of-the-art
baselines and, on top of that, is able to offer an unmatched level of
interpretability. To the best of our knowledge, no previous paper has
quantified the POI influence on charging station utilization from real-world
usage data by estimating the spatial proximity in which POIs are relevant. As
such, our findings help city planners in identifying effective locations for
charging stations.

通过网络挖掘数据，本文提出一种可解释的模型来研究不同兴趣点距离充电站的影响，帮助城市规划师寻找电动汽车充电站的有效位置。

利用网络挖掘确定电动汽车充电站的位置

Web Mining to Inform Locations of Charging Stations for Electric  Vehicles

Point-of-Interest (POI) recommendation has been extensively studied and
successfully applied in industry recently. However, most existing approaches
build centralized models on the basis of collecting users' data. Both private
data and models are held by the recommender, which causes serious privacy
concerns. In this paper, we propose a novel Privacy preserving POI
Recommendation (PriRec) framework. First, to protect data privacy, users'
private data (features and actions) are kept on their own side, e.g., Cellphone
or Pad. Meanwhile, the public data need to be accessed by all the users are
kept by the recommender to reduce the storage costs of users' devices. Those
public data include: (1) static data only related to the status of POI, such as
POI categories, and (2) dynamic data depend on user-POI actions such as visited
counts. The dynamic data could be sensitive, and we develop local differential
privacy techniques to release such data to public with privacy guarantees.
Second, PriRec follows the representations of Factorization Machine (FM) that
consists of linear model and the feature interaction model. To protect the
model privacy, the linear models are saved on users' side, and we propose a
secure decentralized gradient descent protocol for users to learn it
collaboratively. The feature interaction model is kept by the recommender since
there is no privacy risk, and we adopt secure aggregation strategy in federated
learning paradigm to learn it. To this end, PriRec keeps users' private raw
data and models in users' own hands, and protects user privacy to a large
extent. We apply PriRec in real-world datasets, and comprehensive experiments
demonstrate that, compared with FM, PriRec achieves comparable or even better
recommendation accuracy.

本文提出了一种隐私保护的 POI 推荐框架 PriRec，采用本地差分隐私技术保护用户数据、采用安全的去中心化梯度下降协议在用户端训练、采用安全聚合策略在联邦学习范式下训练特征交互模型，实现了用户数据和模型的本地保存和保护，同时具有可比较甚至更优的推荐准确性。