• Journal of Information Processing Systems
• /
• v.17 no.2
• /
• pp.352-368
• /
• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.6 no.12
• /
• pp.3197-3218
• /
• 2012

Compared with traditional itinerary planning, intention-oriented itinerary recommendations can provide more flexible activity planning without requiring the user's predetermined destinations and is especially helpful for those in unfamiliar environments. The rank and classification of points of interest (POI) from location-based social networks (LBSN) are used to indicate different user intentions. The mining of vehicles' physical trajectories can provide exact civil traffic information for path planning. This paper proposes a POI category-based itinerary recommendation framework combining physical trajectories with LBSN. Specifically, a Voronoi graph-based GPS trajectory analysis method is utilized to build traffic information networks, and an ant colony algorithm for multi-object optimization is implemented to locate the most appropriate itineraries. We conduct experiments on datasets from the Foursquare and GeoLife projects. A test of users' satisfaction with the recommended items is also performed. Our results show that the satisfaction level reaches an average of 80%.

• Journal of Communications and Networks
• /
• v.17 no.2
• /
• pp.126-132
• /
• 2015

In mobile computing research area, it is highly desirable to understand the characteristics of user movement so that the user friendly location aware services could be rendered effectively. Location based social networks (LBSNs) have flourished recently and are of great potential for movement behavior exploration and datadriven application design. While there have been some efforts on user check-in movement behavior in LBSNs, they lack comprehensive analysis of social influence on them. To this end, the social-spatial influence and social-temporal influence are analyzed synthetically in this paper based on the related information exposed in LBSNs. The check-in movement behaviors of users are found to be affected by their social friendships both from spatial and temporal dimensions. Furthermore, a probabilistic model of user mobile behavior is proposed, incorporating the comprehensive social influence model with extent personal preference model. The experimental results validate that our proposed model can improve prediction accuracy compared to the state-of-the-art social historical model considering temporal information (SHM+T), which mainly studies the temporal cyclic patterns and uses them to model user mobility, while being with affordable complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.11
• /
• pp.5344-5356
• /
• 2018

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 the form of sign-ups and share their experiences with friends on POI, which can help users to explore new areas and discover new points-of-interest, and promote advertisers to push mobile ads to target users. POI recommendation service in LBSNs is attracting more and more attention from all over the world. Due to the sparsity of users' activity history data set and the aggregation characteristics of sign-in area, conventional recommendation algorithms usually suffer from low accuracy. To address this problem, this paper proposes a new recommendation algorithm based on a novel Preference-Content-Region Model (PCRM). 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 service everywhere. We demonstrate that our algorithm is more effective than existing algorithms through extensive experiments based on an open Eventbrite data set.

• The Journal of the Korea Contents Association
• /
• v.18 no.1
• /
• pp.64-75
• /
• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.

• Journal of Korea Multimedia Society
• /
• v.19 no.2
• /
• pp.428-435
• /
• 2016

Hundreds of millions of new posts and information are being uploaded and propagated everyday on Online Social Networks(OSN) like Twitter, Facebook, or Instagram. This paper proposes and implements a GPS-location based SNS data mapping, analysis, and visualization system, called Smart SNS Map, which collects SNS data from Twitter and Instagram using hundreds of PlanetLab nodes distributed across the globe. Like no other previous systems, our system uniquely supports a variety of functions, including GPS-location based mapping of collected tweets and Instagram photos, keyword-based tweet or photo searching, real-time heat-map visualization of tweets and instagram photos, sentiment analysis, word cloud visualization, etc. Overall, a system like this, admittedly still in a prototype phase though, is expected to serve a role as a sort of social weather station sooner or later, which will help people understand what are happening around the SNS users, systems, society, and how they feel about them, as well as how they change over time and/or space.

• Journal of Digital Contents Society
• /
• v.18 no.3
• /
• pp.567-575
• /
• 2017

Location-based services play an important role in many applications such as disaster warning systems and recommendation systems. These applications often require not only location information (e.g., name, latitude, longitude, etc.) but also the impact of events (e.g., earthquake, typhoon, etc.) on locations. Recently, to provide the impact of an event on a location, how to calculate location trustiness by using multimodal information such as earthquake information and disaster sensor data is researched. In the previous approach, the linear decrement of impact value of an event is applied to obtain the location trustiness of a specific location. In this paper, we propose a new approach to enrich location trustiness, that is, the impact of an event on a location, by using social media information additionally. Firstly, we design a collecting system for earthquake information and social media data. Secondly, we present an approach of location trustiness calculation based on earthquake information. Finally, we propose a new approach to enrich location trustiness by augmenting the trustiness in spatially distributed manner based on social media.

• The Journal of Bigdata
• /
• v.5 no.1
• /
• pp.55-63
• /
• 2020

This study proposed an approach of clustering collective users' activities of location-based social networks using check-in data of Foursquare users in Seoul. In order to cluster the collective activities, we generated sequential rules of the activities using sequential rule mining, and then constructed activity networks based on the rules. We analyzed the activity networks to identify network structure and hub activities, and clustered the activities within the networks. Unlike previous studies that analyzed activity transition patterns of location-based social network users, this study focused on analyzing the structure and clusters of successive activities. Hubs and clusters of activities with the approach proposed in this study can be used for location-based services and marketing. They could also be used in the public sector, such as infection prevention and urban policies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.802-804
• /
• 2017

Nowadays the development of mobile systems occupies significant part of our life, and users using mobile phones are demanding the utilization of differentiated spatial information. A study on application which provides information according to individual location information such as mobile advertisement is conducted for the location-based mobile service. In order to allow mobile phone users to share information based on local information, this paper proposes a mobile sharing system which combines social networks and location-based service to provide efficient information based on location information in mobile phones. The application can be provided with information associated with its location when the mobile phone user arrives at a specific location. Another proposed method focuses on providing more appropriate services for individual situations compared to other applications, and can contribute to expanding social networking functions to facilitate information sharing among users.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.64-77
• /
• 2022

The advent of location-based social networks (LBSNs), and the pervasive use of smartphones have allowed individuals to easily inform their status through locational information. This led to a new trend in social media: to upload geotagged photos that illustrate the location of the images and then share them with others. In this circumstance, the current study aims to examine the use of geotags on Instagram. Further, the motivations for using geotags as well as the relationship among the motivation, satisfaction, and location information sharing behavior are analyzed. The online survey was conducted on 411 respondents of Hong Kong who are active Instagram users. Based on uses and gratification theory and goal theory, the users' motivations and goals for utilizing geotags were divided into mainly two categories; task-involved and self-involved goals. Then, four different motivations (contribution, memory aid, showing off, and reputation gaining) were further examined. The result indicated that contribution, memory aid, and reputation gaining were the goals and motivation for the users to utilize geotags on Instagram, having a positive impact on satisfaction. However, a positive relationship between showing off and geotag satisfaction was not supported. Among four different factors, memory aid was found to have the strongest influence on geotagging satisfaction. The result of testing the relationship between geotag satisfaction and further location information sharing behavior also turned out to have a positive relationship. The implications and limitations of findings are also discussed in the study.