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Network Embedding-Aware Point-of-Interest Recommendation in Location-Based Social Networks

Lei Guo, Haoran Jiang, Xiyu Liu, Changming Xing
2019 Complexity  
., social and geographical information).  ...  As one of the important techniques to explore unknown places for users, the methods that are proposed for point-of-interest (POI) recommendation have been widely studied in recent years.  ...  of POI v from geographical relationships (D t is the dimension of T v ) and Y ∈ R d×D t is the weight matrix that transforms the learned POI representations from location network into the collaborative  ... 
doi:10.1155/2019/3574194 fatcat:yvdlqwr77jahlovqada6e2zs2e

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  
Contingent upon which sort of LBSNs records used to be completely used in buyer displaying forms for POI proposals, we separate client demonstrating calculations into four classifications: pure check-in  ...  This paper centers on evaluating the scientific classification of client displaying for POI proposals through the information investigation of LBSNs.  ...  proposed a personalized ranking metric that embeds a model for the next new POI recommendation.  ... 
doi:10.14419/ijet.v7i3.27.17649 fatcat:xbhdedvfkbe6tgacryx7fbwyke

Points-of-interest Recommendation Algorithm Based on LBSN in Edge Computing Environment

Keyan Cao, Jingjing Guo, Gongjie Meng, Haoli Liu, Yefan Liu, Gui Li
2020 IEEE Access  
Then interact with the geographic information stored in the Cloud to cluster the POIs. And embeds the geographic information into the framework to get the candidate points of interest.  ...  This algorithm effectively integrates the time information and geographic information of users' check-in in the LBSN, and proposes a POIs recommendation algorithm that comprehensively considers edge devices  ...  [7] integrated geographical influence, user preference and social influence into collaborative filtering recommendation, and designed a check-in probability prediction model for a given user's access  ... 
doi:10.1109/access.2020.2979922 fatcat:zaacjuvo35hohceprw54xsqnoq

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).  ...  We first designed a novel variant of GRU, which acquired the user's sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into  ...  [35] proposed a ranking based geographical factorization method, which exploits both geographical and temporal contexts for POI recommendation.  ... 
doi:10.3390/ijgi8080355 fatcat:bwj5scixkjenhkbm6sparmgnjq

Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning

Guanglu Liu, Baiyuan Ding
2022 Scientific Programming  
significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm.  ...  Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively  ...  POI that is close to his frequent area. erefore, geographic factor features can be integrated into the personalized recommendation model, and the user's preference for POI in different geographic locations  ... 
doi:10.1155/2022/8063652 fatcat:673kpgz2dbh4thmmnhxwoepdw4

User Data Driven Recommendation for Location

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
users. complicated methodology is to convey them into explicit- feedback- based mostly content-aware (cf)collaborative filtering, however they have to draw negative attributes for higher learning performance  ...  Final performance show that ICCF outperforms many different competitory baselines,so that user attribute info isn't solely effective for up recommendations however additionally addressing initial knowledge  ...  The effectiveness from the projected distributed and rank one constant plans has been widely estimated, showing its very important profit for rising recommendation, in particular for locations at rich  ... 
doi:10.35940/ijitee.k1223.09811s19 fatcat:7x7cnyn6xfe6vfommyr2zicy4y

On the effects of aggregation strategies for different groups of users in venue recommendation

Pablo Sánchez, Alejandro Bellogín
2021 Information Processing & Management  
In this context, we categorize users into two different groups (tourists and locals) according to their movement patterns and analyze the potential biases in the recommendations received by each of these  ...  In this paper, we address the problem of venue recommendation from a novel perspective: we propose two strategies to select a set of candidate cities in order to use their information when performing recommendations  ...  Acknowledgments This work has been co-funded by the European Social Fund (ESF) within the 2017 call for predoctoral contracts, the Ministerio de  ... 
doi:10.1016/j.ipm.2021.102609 fatcat:5kdcze476zhx3eke7kij6stcui

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence [article]

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
2018 arXiv   pre-print
; (2) the difficulty of incorporating context information such as POIs' geographical coordinates.  ...  The rapid growth of Location-based Social Networks (LBSNs) provides a great opportunity to satisfy the strong demand for personalized Point-of-Interest (POI) recommendation services.  ...  The reason is that DeepAE does not adopt the geographical information which is distinct for POI recommendation.  ... 
arXiv:1809.10770v1 fatcat:4jbtumufrfbefc436kzqxufeku

Point-of-interest lists and their potential in recommendation systems

Giorgos Stamatelatos, George Drosatos, Sotirios Gyftopoulos, Helen Briola, Pavlos S. Efraimidis
2021 Information Technology & Tourism  
Our hypothesis is that the information encoded in the lists can be utilized to estimate the similarities amongst POIs and, hence, these similarities can drive a personalized recommendation system or enhance  ...  The results confirm the existence of rich similarity information within the lists and the effectiveness of our approach as a recommendation system.  ...  We would also like to thank the anonymous reviewers who helped us improve our work, and especially for suggesting the offline experiment of Section 5.3.  ... 
doi:10.1007/s40558-021-00195-5 fatcat:z26fhtad7fhvvgok7hpu7umage

Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism

Landy Rajaonarivo, André Fonteles, Christian Sallaberry, Marie-Noëlle Bessagnet, Philippe Roose, Patrick Etcheverry, Christophe Marquesuzaà, Annig Le Parc Lacayrelle, Cécile Cayèré, Quentin Coudert
2019 ISPRS International Journal of Geo-Information  
This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the  ...  The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype.  ...  The advantage of collaborative filtering approaches is that they allow the recommendation of POIs without requiring specific information about them.  ... 
doi:10.3390/ijgi8050230 fatcat:xg75hw2jcjbodnobogcrpfj7kq

A Graph-Based Taxonomy of Recommendation Algorithms and Systems in LBSNs

Pavlos Kefalas, Panagiotis Symeonidis, Yannis Manolopoulos
2016 IEEE Transactions on Knowledge and Data Engineering  
Recommender systems can exploit this geographic information to provide much more accurate and reliable recommendations to users.  ...  Moreover, we describe and compare extensively 43 state-of-the-art recommendation algorithms for LBSNs.  ...  influence from POIs ðGÞ) combined linearly in a unified collaborative algorithm to recommend locations.  ... 
doi:10.1109/tkde.2015.2496344 fatcat:6gikvhjovvaj5dqvsu7tqvayu4

Computer Highlights Society Magazines

2021 Computer  
a user's geographical and diversity preferences for POI recommendation.  ...  One challenge in POI recommendation is effectively exploiting geographical information since users usually care about the physical distance to the recommended POIs.  ... 
doi:10.1109/mc.2020.3038510 fatcat:xz3u2qnsongczn2r6iafzlk7xm

Personalized Next Point-of-Interest Recommendation via Latent Behavior Patterns Inference [article]

Jing He, Xin Li, Lejian Liao, Williamb K.Cheung
2018 arXiv   pre-print
By integrating categorical influence into mobility patterns and aggregating user's spatial preference on a POI, the proposed model deal with the next new POI recommendation problem by nature.  ...  In this paper, we address the problem of personalized next Point-of-interest (POI) recommendation which has become an important and very challenging task for location-based social networks (LBSNs), but  ...  [6] adopt linear interpolation to incorporate both social and geographical influences into the user-based CF framework for POI recommendation.  ... 
arXiv:1805.06316v1 fatcat:ns4r7irijjet5fk3sg7w5iexgu

Collaborative location and activity recommendations with GPS history data

Vincent W. Zheng, Yu Zheng, Xing Xie, Qiang Yang
2010 Proceedings of the 19th international conference on World wide web - WWW '10  
By using our system, for the first question, we can recommend her to visit a list of interesting locations such as Tiananmen Square, Bird's Nest, etc.  ...  recommendations.  ...  The authors would like to thank Yukun Chen for his great help in data processing. The authors would also thank the reviewers for their helpful comments and suggestions.  ... 
doi:10.1145/1772690.1772795 dblp:conf/www/ZhengZXY10 fatcat:kmtlalp5snghvjtlze3pjy2i54

LCARS

Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, Ling Chen
2014 ACM Transactions on Information Systems  
The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.  ...  The problem becomes even more challenging when people travel to a new city where they have no activity information.  ...  The results also justify each component proposed in our system, for instance, taking into account of local preference and item content information.  ... 
doi:10.1145/2629461 fatcat:cwx5ozfuk5ff5mhakid4d5qjsi
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