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Collaborative filtering meets next check-in location prediction

Defu Lian, Vincent W. Zheng, Xing Xie
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
To this end, in this paper, we leveraged collaborative filtering techniques for check-in location prediction and proposed a short-and long-term preference model.  ...  Though location prediction in terms of check-ins has been recently studied, the phenomena that users often check in novel locations has not been addressed.  ...  (a)A typical scenario for next check-in location prediction. Given three successive checkins(tree icons) of a user(head icon), we predict her next location.  ... 
doi:10.1145/2487788.2487907 dblp:conf/www/LianZX13 fatcat:fnunn3kizbaivay55l5gk7iziy

A Study on Recommendation Systems in Location Based Social Networking

Lakshmi Shree Kullappa, Rajeshwari Kullappa
2017 Journal of Information and Organizational Sciences  
Smart devices in the hands of people are revolutionizing the social lifestyle of one's self.  ...  Users share their experiences at a given location through localization techniques.  ...  Model Based Collaborative Filtering -Here the underlying model /hypothesis is learnt to predict the missing ratings in the User -location Matrix.  ... 
doi:10.31341/jios.41.2.6 fatcat:dgaterc7e5dnfip6vge3ag44hy

Linked Open Data in Location-Based Recommendation System on Tourism Domain: a survey

Phatpicha Yochum, Liang Chang, Tianlong Gu, Manli Zhu
2020 IEEE Access  
Next, we summarize the research achievements and present the distribution of the different categories of location-based recommendation applications via linked open data.  ...  Third, we group the linked open data sources used in location-based recommendation system on tourism.  ...  This strategy reduced the error in the prediction step of the collaborative filtering approach. Liu et al.  ... 
doi:10.1109/access.2020.2967120 fatcat:yqwkrko6mzfw5e5kckfaxbxzju


Defu Lian, Xing Xie, Vincent W. Zheng, Nicholas Jing Yuan, Fuzheng Zhang, Enhong Chen
2015 ACM Transactions on Intelligent Systems and Technology  
We then perform case studies on check-ins and evaluate them on two large-scale check-in datasets with 6M and 36M records, respectively.  ...  CEPR: A collaborative exploration and periodically returning model for location prediction.  ...  Another one may propose to leverage a general collaborative filtering framework (e.g., the sequential collaborative filtering proposed in Lian et al. [2013] ) for location prediction, no matter whether  ... 
doi:10.1145/2629557 fatcat:dx4j73m2rvfzjkmdqv3v7cobda

Current State and Future Trends in Location Recommender Systems

Aysun Bozanta, Birgul Kutlu
2017 International Journal of Information Technology and Computer Science  
It is expected that the issues presented in this paper will advance the discussion of next generation location recommendation systems.  ...  For this purpose, topic pairs; "location and recommender system" and "location and recommendation system" were searched in the Web of Knowledge database.  ...  Location-based social networks allow users to "check-in" at some venues and rate their visits.  ... 
doi:10.5815/ijitcs.2017.06.01 fatcat:rrz2xtgnkfeetd62v6dqh6nzeu

Location Based Social Network For Rating Procedure Geographical Location Using Extended Collaborative Algorithm

M. Muralikrishnan
2017 International Journal Of Engineering And Computer Science  
Check-in behaviors of users will be deeply explored by considering the above factor their multi-activity centers and the attribute of POIs.  ...  and cold start problem, in this paper we make the full use of the deeply exploring the user and check-in user for various categories first, user to user geographical connection distance, then user to  ...  Collaborative filtering methods are classified as memory-based and model based collaborative filtering.  ... 
doi:10.18535/ijecs/v6i5.29 fatcat:34mjaf4g6vb57fueevjlltksbq

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-learning Approaches

Sadaf Safavi, Mehrdad Jalali, Mahboobeh Houshmand
2022 Electronics  
In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places  ...  It found that these articles give priority to accuracy in comparison with other dimensions of quality.  ...  next location.  ... 
doi:10.3390/electronics11131998 fatcat:exuhjcsn3rbw5d3xjsw3aykmhe

An Empirical Recommendation Framework to Support Location-Based Services

Animesh Chandra Roy, Mohammad Shamsul Arefin, A. S. M. Kayes, Mohammad Hammoudeh, Khandakar Ahmed
2020 Future Internet  
In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user's and user-based Collaborative Filtering (CF) to  ...  The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS).  ...  Collaborative Filtering Collaborative Filtering (CF) is a well-known method used in the most RS.  ... 
doi:10.3390/fi12090154 doaj:b799fb039aff4bb7aa15a4101f3337c7 fatcat:i7l35zugd5gkvpqf7akc5fulda

Recommender Systems Meeting Security: From Product Recommendation to Cyber-Attack Prediction [chapter]

Nikolaos Polatidis, Elias Pimenidis, Michalis Pavlidis, Haralambos Mouratidis
2017 Communications in Computer and Information Science  
Then, a recommendation system is utilized to make predictions about future attack steps within the network. We show that recommender systems can be used in cyber defense by predicting attacks.  ...  To do that we initially apply classical collaborative filtering using PCC defined in equation 1.  ...  Attack prediction To recommend attack predictions we use a parameterized version of multi-level collaborative filtering method described in [11] , although other methods could be applied according the  ... 
doi:10.1007/978-3-319-65172-9_43 fatcat:onz52y3h5vel3pb64zoyzuckpm

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Zhen Liu, Huanyu Meng, Shuang Ren, Feng Liu
2017 Security and Communication Networks  
Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN).  ...  A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper.  ...  Next, the converted RDD should be filtered based on the property (hour, weekday).  ... 
doi:10.1155/2017/9127612 fatcat:ckeeydubl5av3oao2do3a44rwy

Discovering Travel Community for POI Recommendation on Location-Based Social Networks

Lei Tang, Dandan Cai, Zongtao Duan, Junchi Ma, Meng Han, Hanbo Wang
2019 Complexity  
We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City.  ...  Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs  ...  For example Berjani and Strufe [21] applied a collaborative filtering (CF) model with check-in data for conducting a POI recommendation.  ... 
doi:10.1155/2019/8503962 fatcat:a5y6gp2mmjbezeoqmu3ojz2mla

Gravity of Location-Based Service: Analyzing the Effects for Mobility Pattern and Location Prediction

Keiichi Ochiai, Yusuke Fukazawa, Wataru Yamada, Hiroyuki Manabe, Yutaka Matsuo
2020 International Conference on Web and Social Media  
Then, we proposed a location prediction method exploiting the characteristics of check-in locations and analyzed how specific LBS usage influences location predictability.  ...  ., check-in locations) that had previously not been revealed.  ...  On the basis of this analysis, we propose a novel location prediction method based on collaborative filtering (CF) that exploits the characteristics of check-in locations.  ... 
dblp:conf/icwsm/OchiaiFYMM20 fatcat:xcgutdq6tngyphxztr7n5qya54


Wagner A. Kamakura
2008 Journal of Relationship Marketing  
In its original formulation (Goldberg et al. 1992) , collaborative filtering used explicit ratings of each e-mail piece by the "collaborators" in the system.  ...  Collaborative Filtering The analytical tools for cross-selling reviewed so far focused on identifying the "next-to-buy" or "next-to-offer" service, based on "natural" acquisition sequences observed across  ... 
doi:10.1300/j366v06n03_03 fatcat:luclynuwxrf4pgwnkywp5mjx2q

A Context-Awareness Personalized Tourist Attraction Recommendation Algorithm

Zhijun Zhang, Huali Pan, Gongwen Xu, Yongkang Wang, Pengfei Zhang
2016 Cybernetics and Information Technologies  
user collaborative filtering technology with friends trust relationships and geographic context.  ...  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

Location recommendation privacy protection method based on location sensitivity division

Chunyong Yin, Xiaokang Ju, Zhichao Yin, Jin Wang
2019 EURASIP Journal on Wireless Communications and Networking  
This method uses location trajectories and check-in frequencies to set a threshold so as to classify location sensitivity levels.  ...  In order to prevent location information from being leaked after monitoring and collection, location privacy must be effectively protected.  ...  Recommendations based on collaborative filtering are also often used for location recommendations [29] .  ... 
doi:10.1186/s13638-019-1606-y fatcat:c2jcppi3wzhjdbv2xk5ukevn3y
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