Recommendation of Points of Interest from User Generated Data Collection

Waga, Karol; Tabarcea, Andrei; Fränti, Pasi

doi:10.4108/icst.collaboratecom.2012.250451

Cited by 17 publications

(8 citation statements)

References 12 publications

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“…An idea behind some recent work is to use geo-referenced online content (e.g., Flickr 10 pictures) to learn and recommend popular trajectories such as (Baraglia et al 2013), as we did in Cicero using Foursquare check-ins to infer popular paths. Others exploit them as sources for mining popular venues (Brilhante et al 2013), travel sequences (Zheng and Xie 2011) or, more in general, travel attractiveness (Waga et al 2012).…”

Section: Itinerary Recommendersmentioning

confidence: 99%

Enhancing cultural recommendations through social and linked open data

Sansonetti

Gasparetti

Micarelli

et al. 2019

User Model User-Adap Inter

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In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user's current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

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Section: Itinerary Recommendersmentioning

confidence: 99%

Enhancing cultural recommendations through social and linked open data

Sansonetti

Gasparetti

Micarelli

et al. 2019

User Model User-Adap Inter

View full text Add to dashboard Cite

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“…Techniques based on signal processing are proposed for including time dimension in context-aware recommendation tasks [9,10]. Online photo sharing services, such as Flickr, or real-world public datasets of rich photographers' histories are often used as sources for mining popular venues [11], travel sequences [12] or, more in general, their attractiveness [13]. The large amount of geo-tagged photos shared on SNS allow LBS to mine also demographic information about the locations by detecting people attributes by means of image analysis techniques.…”

Section: Related Workmentioning

confidence: 99%

A Social Context-Aware Recommender of Itineraries Between Relevant Points of Interest

D’Agostino

Gasparetti

Micarelli

et al. 2016

Communications in Computer and Information Science

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In this paper, we present a personalized recommender system able to suggest to the target user itineraries that both meet her preferences and needs, and are sensitive to her physical and social contexts. The recommendation process takes into account different aspects: in addition to the popularity of the points of interest (POIs), inferred by considering, for instance, the number of check-ins on social networking services such as Foursquare, it also includes the user's profile, the current context of use, and the user's network of social ties. The system, therefore, consists of four main modules that accomplish the following tasks: (1) the construction of the user's profile according to her interests and tastes; (2) the creation of the path graph in the user's proximity; (3) the routing to locate the first k itineraries that match the query; (4) their ranking through a scoring function that considers the POI popularity, the user's profile, and her physical and social context. The proposed system was evaluated on a sample of 40 real users. Experimental results showed the effectiveness of the proposed recommender.

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“…It also supports groups cloud. MOPSI [24] focus on how to mine knowledge from user generated collections without any data cleansing. In this system, the user profile is entirely inferred based on user's activity on the system.…”

Section: Related Workmentioning

confidence: 99%

A Platform for the Recommendation of Points of Interest in Brazilian Cities

Domingues

Santos

Hanada

et al. 2015

Proceedings of the 21st Brazilian Symposium on Multimedia and the Web

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The tourism sector in Brazil has grown considerably in recent years. Despite this growth, the sector still presents several problems such as the lack of information in Portuguese and in other languages for Brazilian and foreign tourists. The absence of information about tourist sites and ordinary services also affects individuals when settling in a new city, as it is the case when freshman students move to a new city to start their studies in a college or university. In this work, we propose an innovative vision of a context-aware platform for recommending points of interest in Brazilian cities, designed with mechanisms for collecting data from the web, for extracting points of interest and background information, and for learning context-aware recommendation models. The platform is accessed by a mobile application. To validate our proposal, we ran a case study where freshman students used the platform during their first months in a new city.

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Recommendation of Points of Interest from User Generated Data Collection

Cited by 17 publications

References 12 publications

Enhancing cultural recommendations through social and linked open data

Enhancing cultural recommendations through social and linked open data

A Social Context-Aware Recommender of Itineraries Between Relevant Points of Interest

A Platform for the Recommendation of Points of Interest in Brazilian Cities

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