ROADIE: MOBILE SEMANTIC TOURISM ROUTES

Andrea Ferracani, Daniele Pezzatini, Alberto Del Bimbo

Universita` degli Studi di Firenze - MICCname.surname@unifi.it

ABSTRACT

Roadie is a mobile application for the planning and the rec-ommendation of tourism routes in cities. Recommendation isachieved profiling users in a semi-automatic way via socialnetwork analysis and profile curation, and by monitoring useractivity analysing data coming from devices physical sen-sors. Routes consist of several POIs (Point Of Interests) thatcan be automatically or manually enriched with geo-eventsand geo-services obtained dynamically from the web, cate-gorised and suggested using semantic analysis.

Index Terms Mobile, tourism, recommendation sys-tems, location-based services

1. INTRODUCTION

Roadie1 is a mobile-based application with two main goals:1) to provide accurate recommendations of city itineraries en-riched with thematic suggestions based on user profiling, 2)to let the user manually create and edit his/her personalisedtours through the city. The main contribution of the applica-tion is to provide a multi-dimensional contextual approach[1]for recommendation. In fact Roadie combines user profil-ing on social networks, location-awareness, semantic analy-sis and activity recognition by sensing in order to improvethe personalisation and the recommendation of city tours inmobile electronic guide systems.

2. THE SYSTEM

Roadie has been developed in Java as a native mobile applica-tion using the Android SDK. The backend is written in PHP.All the data are stored in a MySQL database. The system(Fig. 1) is composed by four core modules which are respec-tively in charge of: 1) grabbing POIs, venues and events, 2)categorising data in realtime, 3) profiling users, 4) recom-mending routes.

2.1. Data Collection

Roadie uses two types of geo-localised data: static data,which doesnt need to be updated frequently, such as POIsand venues, and dynamic data, mainly constituted by events,

1Video available at http://vimeo.com/miccunifi/roadie or downloadablefrom http://bit.ly/1o3V1cY

Fig. 1. Roadie System Architecture.

which change continuously in time. POIs are city attrac-tions retrieved querying the MediaWiki API2. For each POI atextual abstract, a representative image, and place categories(e.g. church, museum, palace, etc.) are collected. GeoNamesAPI3 is exploited in order to get POIs latitude and longitude.Venues are places in the city which can offer visitors com-modities, leisure and entertainment services, such as shops,restaurants, nightclubs etc. These data are obtained usingFoursquare API. Roadie uses events to enrich the visitor expe-rience in the city. Events are characterised by having an exactstart time and duration and are retrieved daily by the grabbingmodule from two sources: the OpenData (for this demo weused data published by the municipality of Florence, IT), andthe Eventful API4. The first is an institutional source whichprovides especially art exhibitions and events, the latter in-stead is user-generated and concerns, for the most, musicalevents and shows.

2.2. User profiling

Roadie builds a model of the user profile of interests exploit-ing user information from the Facebook Graph API throughFacebook Login. User interests are extracted by analysing thecategories of Facebook pages for which the user expressed alike. Additional basic demographic data such as age, genderand residence are also collected. In order to assign profiles tousers that are not able or reluctant to connect their Facebookprofile, we adopt an inference method based only on the de-mographic infos. Given the user u, Roadie looks for similar

2http://bit.ly/RywgdI3http://www.geonames.org/4http://opendata.comune.fi.it/, http://api.eventful.com/

Table 1. User actives and related detected interestActivity Analyzed data Extracted

interestRunning Speed from GPS data SportSightseeing Peaks in Altitude Lookout

registered users and gets a subset of users Su. From Su, themost frequent categories of interest are extracted and assignedto the interest profile of u.

2.2.1. Monitoring user activity

Roadie exploits also physical sensors available in mobile de-vices in order to observe user behaviours and to refine his/herprofile model. We identify two possible meaningful activitiesfor the tourism domain: 1) speed of users movements be-tween geographic positions is monitored in order to estimaterunning/jogging activity, 2) altitude peaks are analysed by aprobabilistic model to assess a user preference for climbingor panoramic views. An asynchronous thread processes datafrom sensors even when the mobile application is in back-ground, allowing a continuous monitoring of the user move-ments. To minimise battery consumption, data are sampledand processed every 60 seconds. Furthermore, the thread isstopped in case of a low battery level (less than 25 %). Activ-ities, when detected, are associated with corresponding cat-egories in the knowledge-base and in user profiles as sum-marised in table 1.

2.2.2. Categorisation of resources and Recommendation

The categorisation module is responsible to classify all the re-sources coming from the grabbing module and to compute asemantic similarity between user preferences and resourcesto be suggested. Roadie adopts a taxonomy of 19 macro-categories that has been manually defined in order to exhaus-tively represent both user interests and venues/events. Sincedata are continuously updated from heterogeneous sources(e.g. events from Eventful, user interests from Facebook,venues and places from Foursquare), uncategorised or labeledwith different categories, Roadie analyses textual informationin order to map these data according to the systems taxon-omy. A max similarity score is computed between each itemc of the taxonomy and a provided or inferred resource cate-gory r using the function sim(c, r) [0, 1]. The correlation isestimated using a semantic text similarity technique[2]. Themethod is based on distributional similarity and Latent Se-mantic Analysis (LSA), further complemented and improvedwith semantic relations extracted from WordNet5. Recom-mendation of routes is based on several factors: context infer-ence, profile of interest computed analysing social networkdata, user activities detected from device sensors. Given theuser location, the recommended routes are built through theMapQuest Route Service6 trying to maximise the ratio be-

5https://wordnet.princeton.edu/6http://mapq.st/1jAVgeL

tween the number of attractions to visit and the available time.Venues, places and events with the highest semantic similarityto the user profile of interests are categorised and suggested,ordered by distance from the user.

3. THE APPLICATION

Roadie is a native mobile application developed in java usingthe Android SDK. The interface is composed by four mainviews: 1) recommended routes, 2) route creation, 3) profileand 4) saved routes. Roadie features Facebook Login as wellas app registration. The profile view shows demographic dataand all the automatically detected interests. These can beedited adding or removing items from the system taxonomy.The recommended routes view provides a scrollable list bywhich the user can select different thematic itineraries on thebasis of his/her interests. For example, if the system detectedsport and literature as the main user interests, the recom-mended route is enriched with sport or public readings eventsgoing on at the moment in the city. All the routes are pre-sented on interactive maps provided by the OpenStreetMap7

service exploiting MapQuest web mapping features. Recom-mended routes can be edited manually directly on the mapor users can utilise the route creation view to plan their visitfrom scratch. To this end Roadie provides smart suggestionsmechanisms which offers an unified perspective for tourist at-tractions and contextual services. First of all, it proposes POIstaking into account user position. Each time the user selectsa POI the system suggests other possible POIs nearby. Other-wise he/she can search for one. Once the route has been de-fined, it can be enriched with venues and events relevant withuser interests and POIs he/she has planned to visit. Imag-ine a use case scenario where a user is building a route thatwill take him from the Florence Cathedral through the Uf-fizi Gallery and then to Piazzale Michelangelo in Florence,IT. If the system detected that sport and food are among userinterests, he/she will be suggested the nearest restaurant tothe Uffizi Gallery for lunch or to take part in a running racescheduled for that day and starting from Piazzale Michelan-gelo. The sport interest may have been manually added bythe user, inferred from Facebook data analysis or detected byRoadie monitoring data from the user smartphone sensors, as-suming that the user is a regular jogger.

References[1] D. Gavalas, V. Kasapakis, C. Konstantopoulos, K. Mas-

takas, and G. Pantziou. A survey on mobile tourism rec-ommender systems. In Communications and InformationTechnology (ICCIT), 2013.

[2] L. Han, A. Kashyap, T. Finin, J. Mayfield, and J. Weese.Umbc ebiquity-core: Semantic textual similarity systems.In Proceedings of the Second Joint Conference on Lexicaland Computational Semantics, 2013.

7http://www.openstreetmap.org/