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Understanding user spatial behaviors for location-based recommendations
2013
Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion
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In this paper, we introduce a network-based method to study user spatial behaviors based on check-in histories. ...
The results of this study have direct implications for location-based recommendation systems. ...
To create stronger location-based recommendations, we need deepen our understanding of human mobility and spatial behavior. Excellent work has been done by González et al. ...
doi:10.1145/2487788.2488096
dblp:conf/www/ZhangTQ13
fatcat:rcmodqslqfbpbdgsonsdqxy3su
User modeling for point-of-interest recommendations in location-based social networks: the state-of-the-art
[article]
2017
arXiv
pre-print
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Depending on which type of LBSNs data was fully utilized in user modeling approaches for POI recommendations, we divide user modeling algorithms into four categories: pure check-in data-based user modeling ...
This paper focuses on reviewing the taxonomy of user modeling for POI recommendations through the data analysis of LBSNs. ...
Far,"Behavior-based location recommendation on location-based social networks," in proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD), pp. 273-285, Jeju, Korea, Ma -y ...
arXiv:1712.06768v1
fatcat:nzmsjj6kjzby7ldi3czf6zlkye
Beyond "local", "categories" and "friends"
2012
Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12
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Here, however, the latent variables which group users can be thought of not as themes but rather as factors which drive check in behaviors, allowing for a qualitative understanding of influences on user ...
Our model is agnostic of geo-spatial location, time, users' friends on social networking sites and the venue categories-we treat the existence of and intricate interactions between these factors as being ...
ACKNOWLEDGEMENTS We would like to thank Justin Cranshaw for the data and for his assistance in developing the concepts presented. ...
doi:10.1145/2370216.2370422
dblp:conf/huc/JosephTC12
fatcat:ejyu7evhwbalzoftjc7soxci4a
A Geographical Factor of Interest Recommended Strategies in Location Based Social Networks
2018
International Journal of Engineering & Technology
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data-based consumer modeling, geographical information-based consumer modeling, spatial-temporal information-based consumer modeling, and geo-social information-based consumer modeling. ...
First, we quickly introduce the shape and records traits of LBSNs, then we current a formalization of user modeling for POI suggestions in LBSNs. ...
Fig. 1 : 1 [1-1.2] Location-based and preference-aware recommendations using sparse check-in data
Fig. 2 : 2 The structure of user-POI data
Fig. 3 : 3 Spatial cluster of user check-ins
Fig. 4 : ...
doi:10.14419/ijet.v7i3.27.17649
fatcat:xbhdedvfkbe6tgacryx7fbwyke
Personalization in Geographic information systems: A survey
[article]
2012
arXiv
pre-print
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not only as spatial objects, but also as maps. ...
Several evaluation criteria are used to identify the existence of trends as well as potential needs for further investigations. ...
Implicit and explicit methods based on screenshots and sound records are used in order to understand the user's behaviors. ...
arXiv:1208.0153v1
fatcat:ommc3m5ylndnbhv47q7r5ph6n4
RESEARCH ON BEHAVIORAL MOTIVATION INFERENCE METHOD FOR GEOGRAPHICAL SPATIOTEMPORAL LARGE DATA
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this paper, based on the application requirements of personalized recommendation information, the GIS platform and related recommendation algorithms are used to fully exploit the user and location based ...
For geographic spatio-temporal big data, this paper proposes a personalized hybrid recommendation algorithm, which is based on users. ...
ALGORITHM FOR USER INTEREST BASED ON GEOGRAPHIC CONTEXT According to the user's geographical situation and user behavior, the association analysis is carried out to understand the situational features ...
doi:10.5194/isprs-archives-xlii-3-w10-725-2020
fatcat:cwnoa3d4rbgbtnrwxmkqe254kq
DeepStore: An Interaction-aware Wide&Deep Model for Store Site Recommendation with Attentional Spatial Embeddings
2019
IEEE Internet of Things Journal
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In order to understand the consumption behaviors of potential consumers at the store and guide the selection of store location, we need to predict the consumption behavior of users in all nearby communities ...
Early studies are based on dedicated models for store site recommendation. Sevtsuk [29] analyzed location patterns of retail and food establishments. ...
Her current research interests include data mining and machine learning, recommender systems, and human activity recognition. ...
doi:10.1109/jiot.2019.2916143
fatcat:jvvpggy245amfnqd4xalebqxle
Similarity-based probabilistic category-based location recommendation utilizing temporal and geographical influence
2016
International Journal of Data Science and Analytics
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Location recommendation on location-based social networks, which is a rapidly growing research topic, suggests recommendations for unvisited locations to their users. ...
This recommendation service is based on users' visit histories and location-related information, such as location categories. ...
The spatial information, such as the geographical position, is also an indication of a user's check-in behavior. For example, users tend to visit locations that are close to their homes or offices. ...
doi:10.1007/s41060-016-0011-4
dblp:journals/ijdsa/ZhouRW16
fatcat:mesmedtrgfh47pox5prqqslioq
Data Analysis on Location-Based Social Networks
[chapter]
2013
Mobile Social Networking
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location-based social networks. ...
Typical location-based social networking sites allow users to "check in" at a physical place and share the location with their online friends, and therefore bridge the gap between the real world and online ...
Therefore, data analysis techniques specifically designed for LBSNs can efficiently deal with these distinct properties, and help understand user behavior for research and business purposes. ...
doi:10.1007/978-1-4614-8579-7_8
fatcat:m2yot724ufe63o5jughtjun33m
Geo-Social Media Analytics
2015
Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion
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Location recommendation is to recommend new locations that users have never visited before. ...
of users, (e) recommender system: item recommendation with geographical and social factors, (f) marketing: devising the marketing strategies based on how users move and interact with each other in the ...
doi:10.1145/2740908.2741985
dblp:conf/www/LiH15
fatcat:wejwlyedfng5fhlx3qfppfvrvy
An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation
2019
ISPRS International Journal of Geo-Information
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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). ...
To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. ...
Spatiotemporal GRU Network Spatial and temporal contextual information are the basis for mining user movement behavior patterns, which help us to understand the behavior background more precisely to improve ...
doi:10.3390/ijgi8080355
fatcat:bwj5scixkjenhkbm6sparmgnjq
Profiling presence patterns and segmenting user locations from cell phone data
[article]
2020
arXiv
pre-print
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person will appear in that location for a given the time of day. ...
on ad-hoc heuristic assignment rules based on the frequency of appearance at given locations. ...
Using behavioral presence pattern to attach contextual understanding to locations have two prominent advantages for event recommendation. ...
arXiv:1805.12208v2
fatcat:e57hgg5nivdc7bsryzxbzdobbu
Managing travel demand: Location recommendation for system efficiency based on mobile phone data
[article]
2016
arXiv
pre-print
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In this paper, we propose a location recommendation system that infers personal preferences while accounting for constraints imposed by road capacity in order to manage travel demand. ...
However, flexible but uncoordinated travel behaviors exacerbate traffic congestion. ...
Call Detail Records, used to understand travel behaviors, have rarely been used to understand and manage travel demand or recommend locations [13, 26, 4] . ...
arXiv:1610.06825v1
fatcat:t33vmnawzradffdwhq4xkjhb74
GeoSneakPique: Visual Autocompletion for Geospatial Queries
[article]
2021
arXiv
pre-print
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Users receive feedback to help them evaluate and refine their spatial selection interactively and can save spatial definitions for re-use in subsequent queries. ...
GeoSneakPique presents a novel method for using a mapping widget to support the NL query process, allowing users to specify location via direct manipulation with data-driven guidance on spatial distributions ...
Recommendations based on cognitive region properties: Visualization recommendation systems are highly data-driven and rely on users' past behavior and preferences. ...
arXiv:2110.12596v1
fatcat:4xf2ltxrezcsvkgkkwtcnf264m
What you are is when you are
2011
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '11
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We extract user check-ins from massive real-world data crawled from Location-based Social Networks to understand the temporal dimension of Points Of Interest. ...
While feature types also underlie most Location-Based Services (LBS), assigning a consistent typing schema for Points Of Interest (POI) across different data sets is challenging. ...
of users in Location-based Social Networks. ...
doi:10.1145/2093973.2093989
dblp:conf/gis/YeJML11
fatcat:3o4qduragvh2hlcmrrtncz7bdy
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