个性化地图挖掘：从地理文本中洞察用户偏好

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本文主要探讨了如何在海量的在线地理文本数据中挖掘用户的个性化偏好，以支持个性化地图服务。作者Kaiqi Zhao、Yiding Liu、Quan Yuan、Lisi Chen、Zhida Chen和Gao Cong分别来自南洋理工大学和香港浸会大学，他们提出了一种名为"Towards Personalized Maps"的方法，通过构建用户行为模型来识别和理解不同类型的用户兴趣。首先，文章指出随着GPS设备的普及，地理文本数据的丰富度与日俱增，这些数据包含了用户的实时位置、活动记录以及他们对地理位置的描述和评论等。这为个性化地图挖掘提供了宝贵的信息来源。与现有的推荐系统和数据分析系统不同，PreMiner（个性化地图挖掘器）更注重于根据用户的个人喜好和行为习惯定制地图体验，包括但不限于： 1. 用户移动性和兴趣挖掘：通过分析用户的位置轨迹和发布的文字内容，系统能够推断出用户的常去地点、兴趣点和活动模式，为用户提供个性化地图导航和兴趣点推荐。 2. 地区意见挖掘：系统能够从用户的评价、评论和反馈中提取观点和情感，从而为特定区域提供有价值的意见总结，帮助用户做出决策。 3. 用户推荐：基于用户的兴趣和行为历史，PreMiner能够精准地为用户推荐相关的地点、活动或服务，提升用户体验。 4. 地理文本数据查询与订阅：用户可以根据自己的需求，对特定类型的数据进行搜索和订阅，如新闻、事件、商业信息等，进一步个性化地图内容展示。 5. 实时性和动态性：由于PreMiner实时处理和更新数据，它能够适应用户需求的变化，提供及时的个性化服务。 "Towards Personalized Maps"方法代表了一种新颖的数据挖掘策略，它不仅提升了地图服务的个性化水平，还为未来的智能城市和位置感知应用开辟了新的研究方向。通过结合用户行为模型和深度学习技术，PreMiner有望成为推动地图领域个性化体验发展的重要工具。

Towards Personalized Maps: Mining User Preferences

from Geo-textual Data

Kaiqi Zhao

Yiding Liu

Quan Yuan

Lisi Chen

Zhida Chen

Gao Cong

Nanyang Technological University

{

kzhao002@e.,

ydliu@,

qyuan1@e.,

chen0936@e.,

gaocong@}ntu.edu.sg

Hong Kong Baptist University

chenlisi@comp.hkbu.edu.hk

ABSTRACT

Rich geo-textual data is available online and the data keeps in-

creasing at a high speed. We propose two user behavior models

to learn several types of user preferences from geo-textual data,

and a prototype system on top of the user preference models for

mining and search geo-textual data (called PreMiner) to support

personalized maps. Different from existing recommender systems

and data analysis systems, PreMiner highly personalizes user ex-

perience on maps and supports several applications, including user

mobility & interests mining, opinion mining in regions, user rec-

ommendation, point-of-interest recommendation, and querying and

subscribing on geo-textual data.

1. INTRODUCTION

People post a variety of content to the internet everyday through

GPS-equiped mobile devices. Such posts are associated with ge-

ographical coordinates (latitude and longitude), and some of them

are associated with semantic places, i.e., points-of-interest (POIs).

They also contain words that imply semantic topics (see Figure

1(a)), or words that imply user’s opinions on different aspects of

a POI (see Figure 1(b)). With multiple types of information avail-

able from geo-textual posts, we face a great opportunity to mine

different kinds of user preferences, including preferences on topic,

region, POI aspect, and category. For example, a user who prefers

topic “sports” may often mention words like “shoot” and “goal” in

their posts. As another example, a user may frequently visit shops

in a shopping area she likes (i.e., preferences on region).

However, building a uniﬁed model that captures different types

of user preferences poses three main challenges. First, the interac-

tions among different types of latent variables (e.g., aspect, senti-

ment, region, topic) and observable variables (e.g., text, time, cat-

egory, POI) are unclear. Second, the data could be in different for-

mats (continuous and discrete) from different data sources (e.g.,

Yelp and Foursquare). The variety of data makes the modeling and

parameter learning complicated. Third, the latent variables in dif-

ferent scopes further complicate the model learning. For example,

each sentence in a review is often related to one aspect and the

This work is licensed under the Creative Commons Attribution-

NonCommercial-NoDerivatives 4.0 International License. To view a copy

of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For

any use beyond those covered by this license, obtain permission by emailing

info@vldb.org.

Proceedings of the VLDB Endowment, Vol. 9, No. 13

(a) Words about topic “eat & drink” in a check-in post

(b) Words about different aspects (environment, food and service)

and their corresponding sentiments in a review

Figure 1: Screenshot of short text (e.g., Foursquare check-ins)

and long text (e.g., Yelp reviews) data

whole review should be posted in some latent region. This implies

that aspect and sentiment are often modeled in the scope of sen-

tence, while region is modeled in the scope of document.

To tackle these challenges, we design two probabilistic mod-

els, namely Who, Where, When, What (W4) model [7, 8] and

Sentiment, Aspect, Region (SAR) model [9] for short text and long

text data, respectively. W4 mines user preferences on topics and re-

gions from short geo-textual documents with temporal information

(e.g., check-ins), while SAR mines user preferences on aspects,

categories and regions from geo-textual documents in which tem-

poral information is not available but the text is long enough for

sentiment analysis (e.g., geo-tagged reviews). Both user behav-

ior models support mining several types of user preferences and

hence cater the needs for various applications. The proposed mod-

els achieve better performances than other models in many appli-

cations. For example, SAR achieves at least 60% higher accuracy

than other models in POI recommendation, and W4 performs at

least 80% more accurate than other models in location prediction.

In this demonstration, we propose a prototype system

, namely

PreMiner, which is built on top of the two user behavior models.

Our system supports querying and mining geo-textual data for per-

sonalized map services, based on the two models and techniques

proposed in our previous work [1, 3]. It supports, but not limited to

the following applications:

The system is available at http://spatialkeyword.sce.

ntu.edu.sg/PreMiner.

1545

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