Mapping Geotagged Tweets to Tourist Spots for Recommender Systems

Oku, Kenji; Ueno, Koki; Hattori, Fumio

doi:10.1109/iiai-aai.2014.159

Cited by 12 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So far, it has received little attention in Twitter data research. In fact, a number of studies involving Twitter have collected only geo-tagged tweets and analyzed those tweets in different domains such as public health (Paul and Dredze 2011), societal events (Ciulla et al 2012), political elections (Skoric et al 2012), tourist spots (Oku et al 2014), and earthquakes (Sakaki et al 2010). However, Cheng et al (2010) reported that only 0.42% tweets are geo-tagged, whereas Morstatter et al (2013) reported that around 3.17% tweets are geo-tagged.…”

Section: Related Workmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

View full text Add to dashboard Cite

Social media is a platform to express one's view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

show abstract

Section: Related Workmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Location inference, in general, can be explained as the retrieval process of the location information from each of textual content, location-specific elements, or the user's social network. A number of studies have focused on the nature of geotagged tweets only, and how this capability can be used to track and analyse different subjects in domains, such as public health [12], societal events [13], political elections [14], tourist spots [15], and earthquakes [16]. However, as mentioned before, geotagged tweets form about 2% of all public tweets broadcasted by Twitter users.…”

Section: Existing Approaches To Location Inferencementioning

confidence: 99%

A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

Laylavi

Rajabifard

Kalantari

2016

IJGI

View full text Add to dashboard Cite

Since its inception, Twitter has played a major role in real-world events-especially in the aftermath of disasters and catastrophic incidents, and has been increasingly becoming the first point of contact for users wishing to provide or seek information about such situations. The use of Twitter in emergency response and disaster management opens up avenues of research concerning different aspects of Twitter data quality, usefulness and credibility. A real challenge that has attracted substantial attention in the Twitter research community exists in the location inference of twitter data. Considering that less than 2% of tweets are geotagged, finding location inference methods that can go beyond the geotagging capability is undoubtedly the priority research area. This is especially true in terms of emergency response, where spatial aspects of information play an important role. This paper introduces a multi-elemental location inference method that puts the geotagging aside and tries to predict the location of tweets by exploiting the other inherently attached data elements. In this regard, textual content, users' profile location and place labelling, as the main location-related elements, are taken into account. Location-name classes in three granularity levels are defined and employed to look up the location references from the location-associated elements. The inferred location of the finest granular level is assigned to a tweet, based on a novel location assignment rule. The location assigned by the location inference process is considered to be the inferred location of a tweet, and is compared with the geotagged coordinates as the ground truth of the study. The results show that this method is able to successfully infer the location of 87% of the tweets at the average distance error of 12.2 km and the median distance error of 4.5 km, which is a significant improvement compared with that of the current methods that can predict the location with much larger distance errors or at a city-level resolution at best.

show abstract

“…For instance, [193] considered the concept of sightseeing spots for different seasons, thus generating seasonal feature vectors for each sightseeing spot, which could support context-aware recommendation of tourist spots depending on the time of the year. Tweets also can be used to characterise the tourist spots [194], or be combined with sentiment analysis to determine the current "mood" of each tourist [195]. [196] opted to work with Twitter and Traveleye in their project.…”

Section: Analysis Of Tourism Recommender Systems Using Social Networkmentioning

confidence: 99%

“…"Comments" were used in 16% of projects, like [202,188] or [190], which extracted items shared by the user in Facebook along with likes, comments and ratings. On the other hand, [193,195,194] worked with tweets in their projects.…”

Section: What Data Are Extracted From Social Network?mentioning

confidence: 99%

“…Only 6% of papers used a "Geotag Weather", tags that contain weather information for a particular location, which helps the development of contextaware systems [97,194,200]; the same figure has the "Rating Items" [185,190], that means, the extraction of ratings such as online evaluations made by the users, which indicates their level of satisfaction (e.g. stars, ranking, likes) regarding restaurants, hotels, cities, POIs, routes, etc.…”

Section: What Data Are Extracted From Social Network?mentioning

confidence: 99%

See 1 more Smart Citation

Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Santos¹

View full text Add to dashboard Cite

I n daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personalitybased recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem

show abstract

Mapping Geotagged Tweets to Tourist Spots for Recommender Systems

Cited by 12 publications

References 7 publications

Event classification and location prediction from tweets during disasters

Event classification and location prediction from tweets during disasters

A Multi-Element Approach to Location Inference of Twitter: A Case for Emergency Response

Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Contact Info

Product

Resources

About