Behavior-based location recommendation on location-based social networks (original) (raw)
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Location recommendation in location-based social networks using user check-in data
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL'13, 2013
This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model.
Location recommendation in location-based
2014
This paper studies the problem of recommending new venues to users who participate in location-based social networks (LBSNs). As an increasingly larger number of users par-take in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this paper, we propose algorithms that create recommendations based on four factors: a...
Location Recommendation based on location-based social networks for Entertainment services
Recent advances in mobile devices permit the use of geographic data in online social networks based on traditional Web site. Which leads to the formation of location based social networks. One of the services provided in these networks is personal location recommendation. While real-world users follow time patterns when they visit the places, so far little attention has been taken to role of the time as an influential factor in users decisions. First, we study user’s behavior based on temporal-spatial patterns and social dimensions of how user’s friend affects user’s decision. Then we model the social, geographical and temporal patterns. We provide a model to recommend location to users. The proposed model examine on real world data of online location social network Foursquare. The results show improvements of our model compare to previous works.
Context-Aware Group-Oriented Location Recommendation in Location-Based Social Networks
ISPRS International Journal of Geo-Information
Location-based social networking services have attracted great interest with the growth of smart mobile devices. Recommending locations for users based on their preferences is an important task for location-based social networks (LBSNs). Since human beings are social by nature, group activities are important in individuals’ lives. Due to the different interests and priorities of individuals, it is difficult to find places that are ideal for all members of a group. In this study, a context-aware group-oriented location recommendation system is proposed based on a random walk algorithm. The proposed approach considers three different contexts, namely users’ contexts (i.e., social relationships, personal preferences), location context (i.e., category, popularity, capacity, and spatial proximity), and environmental context (i.e., weather, day of the week). Three graph models of LBSNs are constructed to perform a random walk with restart (RWR) algorithm in which a user-location graph is ...
A Recommendation System for Spots in Location-Based Online Social Networks
Centralized Online Social Network services (OSN) are collecting immense amounts of data, containing a wealth of information about preferences of their users. Its exploitation for the benefit of the users, even though quite promising, has not seriously been tackled, yet. For this purpose, we propose a personalized recommender for places in location-based OSNs, based on the check-ins of the entire user base. Following a brief analysis, we first propose an interpretation of the data available to OSN providers and an recommendation scheme based on regularized matrix factorization. To evaluate our approach we acquire a large sample of a real data set by crawling Gowalla, one of the most popular location-based
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
The circulation of the social networks and the evolution of the mobile phone devices has led to a big usage of location based social networks application such as Foursquare, Twitter, Swarm and Zomato on mobile phone devices mean that huge dataset which is containing a blend of information about users behaviour's, social society network of each users and also information about each of venues, all these information available in mobile location recommendation system .These datasets are much more different from those which is used in online recommender systems, these datasets have more information and details about the users and the venues which is allowing to have more clear result with much more higher accuracy of the analysing in the result. In this paper we examine the users behaviour's and the popularity of the venue through a large check-ins dataset from a location based social services, Foursquare: by using large scale dataset containing both user check-in and location information .Our analysis expose across 3 different cities.On analysis of these dataset reveal a different mobility habits, preferring places and also location patterns in the user personality. This information about the users behaviour's and each of the location popularity can be used to know the recommendation systems and to predict the next move of the users depending on the categories that the users attend to visit and according to the history of each users check-ins.
Current State and Future Trends in Location Recommender Systems
International Journal of Information Technology and Computer Science
Technological developments in mobile devices enabled the utilization of geographical data for social networks. Accordingly, location-based social networks have become very attractive. The popularity of location-based social networks has prompted researchers to study recommendation systems for location-based services. There are many studies that develop location recommendation systems using various variables and algorithms. However, articles detailing past and present studies, and making future suggestions, are limited. Therefore, this study aims to thoroughly review the research performed on location recommender systems. For this purpose, topic pairs; "location and recommender system" and "location and recommendation system" were searched in the Web of Knowledge database. Resulting articles were examined in detail with respect to data sources and variables, algorithms, and evaluation techniques used. Thus, the current state of location recommender systems research is summarized and future recommendations are provided for researchers and developers. It is expected that the issues presented in this paper will advance the discussion of next generation location recommendation systems.
ACM Computing Surveys
Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.
Place Recommendation Using Location-Based Services And Real-Time Social Network Data
2015
Currently, there is excessively growing information<br> about places on Facebook, which is the largest social network but<br> such information is not explicitly organized and ranked. Therefore<br> users cannot exploit such data to recommend places conveniently and<br> quickly. This paper proposes a Facebook application and an Android<br> application that recommend places based on the number of check-ins<br> of those places, the distance of those places from the current location,<br> the number of people who like Facebook page of those places, and<br> the number of talking about of those places. Related Facebook data is<br> gathered via Facebook API requests. The experimental results of the<br> developed applications show that the applications can recommend<br> places and rank interesting places from the most to the least. We have<br> found that the average satisfied score of the proposed Facebook<br> appli...
An analysis on the impact of geolocation in recommending venues in location-based social networks
2018
The pervasiveness of geo-located devices has opened new possibilities in recommender systems on social networks. In effect, Location-Based Social Networks or LBSNs are a relatively new breed of social networks that let users share their location by triggering ”check-in” events on venues, such as businesses or historical places. In this paper, we compare the performance of traditional rating and social-based similarity metrics against location-based metrics in a userbased collaborative filtering algorithm that recommends venues or places to visit. This analysis was performed on a large real-world dataset provided by the Yelp social network service. Our results show that, geo-located metrics perform as well as rating or social metrics for selecting like-minded users and, thus, to issue a recommendation.