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Research on Point of Interest Recommendation Algorithm Based on Spatial Clustering

Liguo Zheng, Dept. of Computer and Information Engineering School, Harbin Normal University, China
2020 International Journal of Multimedia and Ubiquitous Engineering  
In order to solve the problem of recommendation of points of interest, this paper proposes an algorithm of recommendation of points of interest based on user check-in space clustering.  ...  The final recommendation list is obtained by calculating the recommendation probability of the points of interest.  ...  Conclusion In order to solve the problem of recommendation of points of interest, this paper proposes an algorithm of recommendation of points of interest based on user check-in space clustering.  ... 
doi:10.21742/ijmue.2020.15.1.02 fatcat:spsnotbegzajbiebwuuispzzgi

Mining social networks for local search and location-based recommender systems

Fabio Gasparetti, Damianos Gavalas, Sergio Ilarri, Francesco Ricci, Zhiwen Yu
2019 Personal and Ubiquitous Computing  
Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ...  We would like to thank especially the authors of the accepted papers for their effort in revising and improving their work (occasionally, several times) in response to reviewers' comments.  ...  The guest editors would also like to thank the support of some projects that fund their research activities in relation to the topics of the special issue, such as the project TIN2016-78011-C4-3-R (AEI  ... 
doi:10.1007/s00779-019-01241-0 fatcat:4uhdujarjbe47maeang2j6aa5e

CNNA: A study of Convolutional Neural Networks with Attention

Qinghua Zhang
2021 Procedia Computer Science  
Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are not really  ...  Abstract Point-of-Interest (POI) recommendation systems can give the user a list of locations that the user may be interested in, but the recent check-ins may contain some daily check-ins that users are  ...  Acknowledgements This work is supported by Research Program of Educational and Teaching Reform in Hunan Vocational Colleges (ZJGB2020113).  ... 
doi:10.1016/j.procs.2021.05.049 fatcat:og3phbfdajaj5f7yvugptbayve

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  
With the advancement of the Internet of Everything era and the popularity of mobile devices, Location-based Social Networks (LBSN) have penetrated people's lives.  ...  It is a key research direction based on location recommendation to accurately obtain the places of interest of users and push them to clients in such a large amount of original data.  ...  Location selection is crucial in geo-social network, Zhong et al. [14] proposed the problem of sample location selection to maximize the influence of distance perception in the geo-social network.  ... 
doi:10.1109/access.2020.2979922 fatcat:zaacjuvo35hohceprw54xsqnoq

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Zhijun Zhang, Huali Pan, Gongwen Xu, Yongkang Wang, Pengfei Zhang
2016 Cybernetics and Information Technologies  
With the rapid development of social networks, location based social network gradually rises.  ...  In order to retrieve user's most preferred attractions from a large number of tourism information, personalized recommendation algorithm based on the geographic location has been widely concerned in academic  ...  User-based collaborative filtering algorithm In the location-based social network, users and location are linked by check-in.  ... 
doi:10.1515/cait-2016-0084 fatcat:vjvghekjqvdklnky5bmpghl75a

A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

Logesh Ravi, Subramaniyaswamy Vairavasundaram
2016 Computational Intelligence and Neuroscience  
Recent research on recommender systems reveals an idea of utilizing social network data to enhance traditional recommender system with better prediction and improved accuracy.  ...  This paper expresses views on social network data based recommender systems by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering  ...  Three groups of location based social networking services are geotagged-media-based services, point location based services, and trajectory-based services. (i) Geotagged-Media-Based.  ... 
doi:10.1155/2016/1291358 pmid:27069468 pmcid:PMC4812910 fatcat:ddeoidjh2rgq5o5cugtdke7o7i

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

2018 ISPRS International Journal of Geo-Information  
Location-based social networks (LBSNs) provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations.  ...  The preferences of the users, proximity of the places the users have visited in terms of spatial range, users' free days, and the social relationships among users are extracted automatically from location  ...  Figure 1 . 1 Data Structures in Location-Based Social Networks: (a) Overview of a location-based social network (adapted from [27]), (b) Detailed location category hierarchy in Foursquare.  ... 
doi:10.3390/ijgi7020067 fatcat:2fwbiroicfeobnkqsqfv4myptu

Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

Heba M. Wagih, Hoda M. O. Mokhtar, Samy S. Ghoniemy
2019 ISPRS International Journal of Geo-Information  
Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented  ...  The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.  ...  Funding: This work was supported by a fund from The British University in Egypt. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi8090415 fatcat:e5cttykanfbljpyuhpqazd2bby

GuideMe – A Tourist Guide with a Recommender System and Social Interaction

Artem Umanets, Artur Ferreira, Nuno Leite
2014 Procedia Technology - Elsevier  
As compared to previous recommender based tourist guides, the key novelties of GuideMe are its integration with social networks and the unique set of options offered in the application.  ...  Each user may consult places of touristic interest, receive suggestions of previously unseen touristic places according to other users recommendations, and to perform its own recommendations.  ...  The experimental evaluation of the developed RS is presented in Section 6.  ... 
doi:10.1016/j.protcy.2014.10.248 fatcat:4ekupcbx4bd3lkrsrwlm6j264e

Deep CNN-Assisted Personalized Recommendation over Big Data for Mobile Wireless Networks

Yu Zheng, Xiaolong Xu, Lianyong Qi
2019 Wireless Communications and Mobile Computing  
Furthermore, we recommend the potential visiting locations for mobile users through the deep learning CNN network with the social and mobile trajectory big data.  ...  Specifically, we acquire the location information and moving trajectory sequence in the mobile wireless network first.  ...  Acknowledgments The work was funded by the National Natural Science Foundation of China (Grants nos. 61702277 and 61872219).  ... 
doi:10.1155/2019/6082047 fatcat:62ul4n65bbhxhjp5pukc3enp6u

PCRM: Increasing POI Recommendation Accuracy in Location-Based Social Networks

2018 KSII Transactions on Internet and Information Systems  
Nowadays with the help of Location-Based Social Networks (LBSNs), users of Point-of-Interest (POI) recommendation service in LBSNs are able to publish their geo-tagged information and physical locations  ...  In this new algorithm, three kinds of information, that is, user's preferences, content of the Point-of-Interest and region of the user's activity are considered, helping users obtain ideal recommendation  ...  In this context, Location-Based Social Networks (LBSNs) are rapidly growing and related location-based service are being loved by the majority of users [1, 2] .  ... 
doi:10.3837/tiis.2018.11.010 fatcat:7nvj74rikffyjl7m3rnuj7gsuq

RecPOID: POI Recommendation with Friendship Aware and Deep CNN

Sadaf Safavi, Mehrdad Jalali
2021 Future Internet  
In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant.  ...  In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/fi13030079 fatcat:qe2wpeijyjhpbe7rqrzysodzrm

User Group-based Method for Cold-Start Recommendation

Jing He
2018 International Journal of Performability Engineering  
Therefore, a dynamic cold-start recommendation algorithm would be highly helpful in such quick-changing social networks.  ...  A series of experiments involving collection of a huge data set was developed to evaluate the effectiveness of UCFRA. The experimental results showed that UCFRA is a valid algorithm.  ...  Acknowledgements This work is supported by the National Natural Science Foundation of China (61762089, 61663047) and the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province (2017SE206  ... 
doi:10.23940/ijpe.18.08.p8.17191725 fatcat:7l4vllo7r5ctfmvzxpog3kdeki

A travel route recommendation algorithm based on interest theme and distance matching

Xi Cheng
2021 EURASIP Journal on Advances in Signal Processing  
AbstractTo solve the problem of low accuracy of traditional travel route recommendation algorithm, a travel route recommendation algorithm based on interest theme and distance matching is proposed in this  ...  Then, the user's preferences of interest theme and distance matching are proposed based on the user's stay in each scenic spot.  ...  How to effectively mine valuable information from social network information plays an irreplaceable role in the development of social network [12] .  ... 
doi:10.1186/s13634-021-00759-x fatcat:wexejiupqjf25n2yh2sxvuae2a

Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces [chapter]

Emanuel Lacic, Dominik Kowald, Lukas Eberhard, Christoph Trattner, Denis Parra, Leandro Balby Marinho
2014 Lecture Notes in Computer Science  
To contribute to this sparse field of research, in this paper we exploit users' interactions along three data sources (marketplace, social network and location-based) to assess their performance in a barely  ...  studied domain: recommending products and domains of interests (i.e., product categories) to people in an online marketplace environment.  ...  Moreover, parts of this work were carried out during the tenure of an ERCIM "Alain Bensoussan" fellowship programme.  ... 
doi:10.1007/978-3-319-14723-9_6 fatcat:dre5bco3tva7nkjmwm43jhxlpu
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