Filters








64,517 Hits in 6.9 sec

Understanding user spatial behaviors for location-based recommendations

Jun Zhang, Chun-yuen Teng, Yan Qu
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
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]

Shudong Liu
2017 arXiv   pre-print
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"

Kenneth Joseph, Chun How Tan, Kathleen M. Carley
2012 Proceedings of the 2012 ACM Conference on Ubiquitous Computing - UbiComp '12  
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

Bulusu Rama, K Sai Prasad, Ayesha Sultana, K Shekar
2018 International Journal of Engineering & Technology  
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]

Saida Aissi, Mohamed Salah Gouider
2012 arXiv   pre-print
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

J. W. Li, W. D. Chen, Y. Ma, N. Yu, X. Li, J. W. Jiang
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
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

Yan Liu, Bin Guo, Nuo Li, Jing Zhang, Jingmin Chen, Daqing Zhang, Yinxiao Liu, Zhiwen Yu, Sizhe Zhang, Lina Yao
2019 IEEE Internet of Things Journal  
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

Dequan Zhou, Seyyed Mohammadreza Rahimi, Xin Wang
2016 International Journal of Data Science and Analytics  
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]

Huiji Gao, Huan Liu
2013 Mobile Social Networking  
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

Cheng-Te Li, Hsun-Ping Hsieh
2015 Proceedings of the 24th International Conference on World Wide Web - WWW '15 Companion  
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

Chunyang Liu, Jiping Liu, Jian Wang, Shenghua Xu, Houzeng Han, Yang Chen
2019 ISPRS International Journal of Geo-Information  
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]

Yan Leng, Haris Koutsopoulos, Jinhua Zhao
2020 arXiv   pre-print
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]

Yan Leng, Alex 'Sandy' Pentland, Haris N. Koutsopolous
2016 arXiv   pre-print
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]

Vidya Setlur, Sarah Battersby, Tracy Wong
2021 arXiv   pre-print
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

Mao Ye, Krzysztof Janowicz, Christoph Mülligann, Wang-Chien Lee
2011 Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '11  
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
« Previous Showing results 1 — 15 out of 64,517 results