Location Recommendation with Social Media Data

Bothorel, Cécile; Lathia, Neal; Picot-Clémente, Romain; Noulas, Anastasios

doi:10.1007/978-3-319-90092-6_16

Cited by 29 publications

(17 citation statements)

References 59 publications

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“…However, the work focused on recommending POIs such as restaurants or coffee shops using online social connections are still rare. Some of this work is reviewed in the Chapter 16 of this book [16].…”

Section: Applications and Target Items Of Social Link-based Recommendmentioning

confidence: 99%

“…Because social recommendations is actively evolving field of research, depending on the purpose of each study, researchers have been investigating social recommendation technologies in various ways. For instance, the studies constituting 'social recommendations' include recommenders to utilize implicitly inferred social links as its foundations of recommendations; to suggest person(s) to be connected (as introduced in Chapter 16 [16]); or to recommend items to a group of users (For more detailed explanations about various kinds of social recommendations, refer to section 12.2). Through personalized recommendations of items based on users' explicit social networks, we will examine how researchers harness the power of users' various social interactions and the resultant social phenomenon (e.g.…”

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confidence: 99%

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Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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Section: Applications and Target Items Of Social Link-based Recommendmentioning

confidence: 99%

mentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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“…To improve the user satisfaction, the point-of-interest (POI) recommendation is proposed to capture the user preferences based on sufficient information (e.g., check-ins and ratings) and push potential desired POIs to individuals. Therefore, the research of POI recommender systems becomes increasingly popular, and researchers make many efforts on the effectiveness and efficiency of POI recommendation [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

et al. 2020

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The rapid development of location-based social networks (LBSNs) produces the increasing number of check-in records and corresponding heterogeneous information which bring big challenges of points-of-interest (POIs) recommendation in our daily lives. The emergence of various recommender techniques bridges the gap between the numerous heterogeneous check-ins and the personalized POI recommendation. However, due to the differences between LBSNs and conventional recommendation tasks, besides the user feedback, the spatio-temporal information is also significant to precisely capture the user preferences. In this paper, we propose a multi-task learning model based POI recommender system which exploits a structure of generative adversarial networks (GAN) simultaneously considering temporal check-ins and geographical locations. The GAN-based model is capable of relieving the sparsity of check-in data in POI recommender systems. The temporal check-ins not only present the preference but also show the lifestyle of an individual while the geographical locations describe the active region of users which further filters POIs far from the feasible region. The multi-task learning strategy is capable of combining the information of temporal check-ins and geographical locations to improve the performance of personalized POI recommendation. We conduct the experiments on two real-world LBSNs datasets and the experimental results show the effectiveness of our proposed approach.

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“…In the last decade, the diffusion of mobile devices has promoted a rich virtual social environment and a growing interaction with location-based services and social media for everyday tasks such as accessing local news, consulting mobility information, share opinions, etc. (Bothorel et al, 2018). Besides the private use of these technologies, the opportunity of sharing content in such a direct and informal way has definitely attracted the attention of many actors involved in both the business sector (Jussila et al, 2014) as well as in the public administration (Mergel and Bretschneider, 2013).…”

Section: Introductionmentioning

confidence: 99%

User Geolocated Content Analysis for Urban Studies: Investigating Mobility Perception and Hubs Using Twitter

Molinari¹,

Oxoli²,

Kilsedar³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The availability of content constantly generated within theWeb has resulted in an incredibly rich virtual social environment from which it is possible to retrieve almost any sort of information. Since the advent of the social media connection with location-based services, this information has attracted the interest of manifold disciplines connected to the spatial data science. In this context, we introduce the URBAN-GEO BIG DATA (URBAN GEOmatics for Bulk Information Generation, Data Assessment and Technology Awareness), a Project of National Interest funded by the Italian Ministry of Education that aims at contributing to the exploitation of heterogeneous geodata sources such as VGI, geo-crowdsourcing, earth observation, etc. for a better understanding of urban dynamics. The presented work tackles one of the tasks requested by the project, which is connected to an investigation of the use of Twitter as a geodata source for retrieving valuable insights on the citizens’ interaction with mobility services and hubs. The study refers to five Italian cities, namely Milan, Turin, Padua, Rome, and Naples. Data collection is performed through the use of the Twitter streaming application programming interface. Collected data is analyzed by means of natural language processing techniques with Python. Results include a) extractions of mobility-related tweets presented by means of maps enabling the exploration of their spatial distribution within the cities, and b) a classification of the mobility-related tweets by means of sentiment analysis, allowing to investigate citizens’ perceptions of mobility services. A light and reproducible procedure to achieve these results is also outlined. In general terms, the results are intended for providing snapshots of the citizen interaction with both mobility infrastructure and services enabling a better description of mobility patterns and habits within the studied cities. The work leverages the geo-crowdsourced data within the traditional urban management practices in Italy and investigates the benefits, drawbacks, limitations connected to these data sources, which is the ultimate goal of the URBAN-GEO BIG DATA project.</p>

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Location Recommendation with Social Media Data

Cited by 29 publications

References 59 publications

Recommendations Based on Social Links

Recommendations Based on Social Links

MTPR: A Multi-Task Learning Based POI Recommendation Considering Temporal Check-Ins and Geographical Locations

User Geolocated Content Analysis for Urban Studies: Investigating Mobility Perception and Hubs Using Twitter

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