LBSNs中POI推荐系统的研究综述

POI

兴趣点推荐

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"这篇资源是一篇关于点兴趣（POI）推荐的综述文章，主要探讨了基于位置的社会网络（LBSNs）中的POI推荐系统的重要性和研究方向。作者们来自南京邮电大学，他们详细阐述了POI推荐的独特特征，并讨论了未来的研究趋势。" 在当今移动设备、全球定位系统（GPS）和Web2.0技术快速发展的时代，基于位置的社会网络（LBSNs）如雨后春笋般涌现，吸引了数百万用户分享丰富的信息，包括个人体验和建议。点兴趣（Point-of-Interest, POI）推荐系统在LBSNs中扮演着至关重要的角色，它不仅帮助用户发现有趣的位置，还能辅助社交网络服务提供商设计基于位置的广告，以吸引用户到特定的点兴趣。 POI推荐的独特特征主要包括以下几点： 1. 用户行为与地理位置的关联性：用户的活动轨迹和兴趣往往与其所在位置密切相关，推荐系统需要理解并利用这种关联性，为用户推荐符合其位置偏好的POI。 2. 时间上下文：用户在不同时间可能对不同的POI有不同的需求，比如工作日和周末、白天和夜晚的偏好可能有所不同，推荐系统需要考虑这些时间因素。 3. 社交网络信息：用户的朋友圈、互动和分享的信息是理解用户兴趣的重要线索，推荐系统可以利用社交网络数据来提升推荐的准确性。 4. 冷启动问题：新用户或新POI可能没有足够的历史记录，推荐系统需要有策略地处理这些情况，如利用全局信息或依赖其他用户的行为模式。 5. 动态环境：LBSNs环境不断变化，新的POI不断出现，旧的POI可能会消失或更新，推荐系统必须能够适应这些变化。 6. 隐私考虑：在处理用户位置数据时，推荐系统必须确保用户的隐私得到保护，避免敏感信息泄露。在研究方向上，该综述文章可能涵盖了以下几点： 1. 深度学习和多模态融合：利用深度学习模型处理复杂的位置和用户行为数据，结合图像、文本等多模态信息，提高推荐的精准度。 2. 时空预测模型：开发更精确的时空预测算法，以预测用户未来可能感兴趣的位置。 3. 实时和个性化推荐：构建实时响应的推荐系统，能够根据用户的即时状态和环境提供个性化推荐。 4. 可解释性和透明度：提升推荐系统的可解释性，让用户了解推荐背后的原因，增强用户信任度。 5. 社区发现和群组推荐：识别用户群体的兴趣模式，实现针对社区或群体的推荐，提高推荐的覆盖率和满意度。 6. 在线评估和优化：设计更有效的在线评估方法，实时调整推荐策略，以适应不断变化的用户行为和环境。通过这样的综述，研究人员和从业者可以了解到POI推荐领域的最新进展，为未来的研究和应用提供参考。

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A Survey of Point-of-Interest Recommendation

in Location-Based Social Networks

Yonghong Yu

Tongda College

Nanjing University of Posts and Telecommunications

yuyh@njupt.edu.cn

Xingguo Chen

School of Computer Science and Technology

School of Software

Nanjing University of Posts and Telecommunications

chenxg@njupt.edu.cn

Abstract

With the rapid development of mobile devices, global

position system (GPS) and Web 2.0 technologies,

location-based social networks (LBSNs) have attracted

millions of users to share rich information, such as

experiences and tips. Point-of-Interest (POI) recom-

mender system plays an important role in LBSNs since

it can help users explore attractive locations as well as

help social network service providers design location-

aware advertisements for Point-of-Interest. In this pa-

per, we present a brief survey over the task of Point-of-

Interest recommendation in LBSNs and discuss some

research directions for Point-of-Interest recommenda-

tion. We ﬁrst describe the unique characteristics of

Point-of-Interest recommendation, which distinguish

Point-of-Interest recommendation approaches from tra-

ditional recommendation approaches. Then, according

to what type of additional information are integrated

with check-in data by POI recommendation algorithms,

we classify POI recommendation algorithms into four

categories: pure check-in data based POI recommenda-

tion approaches, geographical inﬂuence enhanced POI

recommendation approaches, social inﬂuence enhanced

POI recommendation approaches and temporal inﬂu-

ence enhanced POI recommendation approaches. Fi-

nally, we discuss future research directions for Point-

of-Interest recommendation.

Introduction

With the rapid development of mobile devices, global po-

sition system (GPS) and Web 2.0 technologies, location-

based social networks (LBSNs) have become very popular

and attracted lots of attention from industry and academia.

Typical location-based social networks include Foursquare,

Gowalla, Facebook Place, and GeoLife, etc.. In LBSNs,

users can build connections with their friends, upload pho-

tos, and share their locations via check-in for points of inter-

est (e.g., restaurants, tourists spots, and stores, etc.). Besides

providing users with social interaction platforms, it is more

desired for LBSNs to make use of the rich information (so-

cial relationships, check-in history and so on) to mine users’

preferences on locations and recommend new places where

users may be interested in. The task of recommending new

 2015, Association for the Advancement of Artiﬁcial

interesting places is referred as point-of-interest (POI) rec-

ommendation. POI recommender systems have played an

important role in LBSNs since they can not only meet users’

personalized preferences for visiting new places, but also

help LBSNs to increase revenues by providing users with

intelligent location services, such as location-aware adver-

tisements.

Although recommender systems have been widely studied

and successfully adopted by many e-commerce web sites,

such as Amazon, Netﬂix, Last.fm and Taobao etc., POI rec-

ommender systems have just emerged recently. Differing

from traditional recommender systems, POI recommender

systems have the following unique characteristics.

• Geographical Inﬂuence. As the Tobler’s First Law of

Geography reported that ”Everything is related to every-

thing else, but near things are more related than distant

things” (Tobler 1970). For LBSNs, the Tobler’s First Law

of Geography implies that users prefer to visit nearby lo-

cations rather than distant ones and users may be inter-

ested in POIs surrounded a POI that users prefer. Geo-

graphical Inﬂuence is the most important characteristic

that distinguish POI recommender systems from tradi-

tional recommender systems and heavily effect users’ vis-

iting behaviors.

• Frequency Data and Sparsity. In traditional recom-

mender systems, user generally expressed their prefer-

ences by explicitly providing ratings for items (e.g., book,

movie, music and so on), which are converted to user-

item rating matrix. The ratings are often numerical values

and fall into a numerical range, such as [1,5]. The higher

rating corresponds the better satisfactory. Unlike to tradi-

tional recommender systems, a user’s preferences are re-

ﬂected by the frequency of check-in for locations, which

are often transformed to user-location check-in frequency

matrix. The frequency data have a large range compared

with ratings. For example, user may check in thousands

of times at some locations, while user may check in few

times for other locations. In addition, the sparsity of user-

location check in frequency matrix is dramatically higher

than that of user-item rating matrix, which leads to big-

ger challenge for POI recommendation. For example, the

sparsity of Netﬂix data set is around 99%, while the spar-

sity of Gowalla is about 2.08 × 10

−4

Trajectory-Based Behavior Analytics: Papers from the 2015 AAAI Workshop

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LBSNs中POI推荐系统的研究综述

biocondutor case studies

基于用户相似度和社会关系和地理位置三个因素的POI推荐系统(Foursquare数据和python代码)

Unified Point-of-Interest Recommendation with temporal interval assessment

Efficient Video Recommendation with Multi-Head Self-Attention and Hybrid Sampling

Concept-aware deep knowledge tracing and exercise recommendation in an online learning system

数据结构知识图谱构建与可视化英文参考文献

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session-based recommendation

Traceback (most recent call last): File "D:\开源代码\00 表情识别\Emotion-Music-Recommendation-main\Emotion-Music-Recommendation-main\camera.py", line 4, in <module> from tensorflow import keras ImportError: cannot import name 'keras' from 'tensorflow' (unknown location)

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