Recommending places blased on the wisdom-of-the-crowd

The collective opinion of a great number of users, popularly known as wisdom of the crowd, has been seen as powerful tool for solving problems. As suggested by Surowiecki in his books [134], large groups of people are now considered smarter than an elite few, regardless of how brilliant at solving problems or coming to wise decisions they are. This phenomenon together with the availability of a huge amount of data on the Web has propitiated the development of solutions which employ the wisdom-of-the-crowd to solve a variety of problems in different domains, such as recommender systems [128], social networks [100] and combinatorial problems [152, 151]. The vast majority of data on the Web has been generated in the last few years by billions of users around the globe using their mobile devices and web applications, mainly on social networks. This information carries astonishing details of daily activities ranging from urban mobility and tourism behavior, to emotions and interests. The largest social network nowadays is Facebook, which in December 2015 had incredible 1.31 billion mobile active users, 4.5 billion “likes” generated daily. In addition, every 60 seconds 510 comments are posted, 293, 000 statuses are updated, and 136,000 photos are uploaded1. This flood of data has brought great opportunities to discover individual and collective preferences, and use this information to offer services to meet people’s needs, such as recommending relevant and interesting items (e.g. news, places, movies). Furthermore, it is now possible to exploit the experiences of groups of people as a collective behavior so as to augment the experience of other. This latter illustrates the important scenario where the discovery of collective behavioral patterns, the wisdom-of-the-crowd, may enrich the experience of individual users. In this light, this thesis has the objective of taking advantage of the wisdom of the crowd in order to better understand human mobility behavior so as to achieve the final purpose of supporting users (e.g. people) by providing intelligent and effective recommendations. We accomplish this objective by following three main lines of investigation as discussed below. In the first line of investigation we conduct a study of human mobility using the wisdom-of- the-crowd, culminating in the development of an analytical framework that offers a methodology to understand how the points of interest (PoIs) in a city are related to each other on the basis of the displacement of people. We experimented our methodology by using the PoI network topology to identify new classes of points of interest based on visiting patterns, spatial displacement from one PoI to another as well as popularity of the PoIs. Important relationships between PoIs are mined by discovering communities (groups) of PoIs that are closely related to each other based on user movements, where different analytical metrics are proposed to better understand such a perspective. The second line of investigation exploits the wisdom-of-the-crowd collected through user-generated content to recommend itineraries in tourist cities. To this end, we propose an unsupervised framework, called TripBuilder, that leverages large collections of Flickr photos, as the wisdom-of- the-crowd, and points of interest from Wikipedia in order to support tourists in planning their visits to the cities. We extensively experimented our framework using real data, thus demonstrating the effectiveness and efficiency of the proposal. Based on the theoretical framework, we designed and developed a platform encompassing the main features required to create personalized sightseeing tours. This platform has received significant interest within the research community, since it is recognized as crucial to understand the needs of tourists when they are planning a visit to a new city. Consequently this led to outstanding scientific results. In the third line of investigation, we exploit the wisdom-of-the-crowd to leverage recommendations of groups of people (e.g. friends) who can enjoy an item (e.g. restaurant) together. We propose GroupFinder to address the novel user-item group formation problem aimed at recommending the best group of friends for a pair. The proposal combines user-item relevance information with the user’s social network (ego network), while trying to balance the satisfaction of all the members of the group for the item with the intra-group relationships. Algorithmic solutions are proposed and experimented in the location-based recommendation domain by using four publicly available Location-Based Social Network (LBSN) datasets, showing that our solution is effective and outperforms strong baselines

(So) Big Data and the transformation of the city

The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality

Reputation evaluation of georeferenced data for crowd-sensed applications

Volunteered Geographic Information (VGI) is a process where individuals, supported by enabling technologies, behave like physicalsensorstoharvestgeoreferencedcontentintheirsurroundings. Thevalueofthis, typicallyheterogeneous, contenthasbeen recognized by both researchers and organizations. However, in order to be fruitfully used in various VGI-based types of application reliability and quality of particular VGI content (i.e., Points of Interest) have to be assessed. This evaluation can be based on reputation scores that summarize users’ experiences with the specific content. Following this direction, our contribution provides, primarily, a new comprehensive model and a multi-layer architecture for reputation evaluation aimed to assess quality of VGI content. Secondly, we demonstrate the relevance of adopting such a framework through an applicative scenario for recommending touristic itineraries

Evaluating Reputation in VGI-enabled Applications

ABSTRACT Volunteered Geographic Information (VGI) is an approach to crowdsource information about geospatial objects around us, as implemented in Open Street Map, Google Map Maker and WikiMapia projects. The value of this content has been recognized by both researchers and organizations for acquiring free, timely and detailed spatial data versus standard spatial data warehouses where objects are created by professionals with variable updating time. However, evaluating its quality and handling its heterogeneity remain challenging concerns. For instance, VGI data sources have been compared to authoritative geospatial ones on specific regions/areas in order to determine an average overall quality level. In user-oriented VGI-based applications, it can be more relevant to assess the quality of particular contents, like specific Points of Interest. In this case, evaluation can be performed indirectly by reputation scores associated with the specific content. This paper focuses on this last aspect. Our contribution primarily provides a comprehensive model and architecture for reputation evaluation aimed to assess quality of VGI content. On the other hand, we also focus on applications by discussing two motivating scenarios for reputation-enhanced VGI data in the context of geospatial decision support systems and in recommending tourist itineraries

POIBERT: A Transformer-based Model for the Tour Recommendation Problem

Tour itinerary planning and recommendation are challenging problems for tourists visiting unfamiliar cities. Many tour recommendation algorithms only consider factors such as the location and popularity of Points of Interest (POIs) but their solutions may not align well with the user's own preferences and other location constraints. Additionally, these solutions do not take into consideration of the users' preference based on their past POIs selection. In this paper, we propose POIBERT, an algorithm for recommending personalized itineraries using the BERT language model on POIs. POIBERT builds upon the highly successful BERT language model with the novel adaptation of a language model to our itinerary recommendation task, alongside an iterative approach to generate consecutive POIs. Our recommendation algorithm is able to generate a sequence of POIs that optimizes time and users' preference in POI categories based on past trajectories from similar tourists. Our tour recommendation algorithm is modeled by adapting the itinerary recommendation problem to the sentence completion problem in natural language processing (NLP). We also innovate an iterative algorithm to generate travel itineraries that satisfies the time constraints which is most likely from past trajectories. Using a Flickr dataset of seven cities, experimental results show that our algorithm out-performs many sequence prediction algorithms based on measures in recall, precision and F1-scores.Comment: Accepted to the 2022 IEEE International Conference on Big Data (BigData2022

Valoriser le patrimoine culturel pyrénéen à l’aide d’une plateforme et d’une application mobile

Le projet TCVPYR est un projet européen FEDER dont le but est de promouvoir le tourisme dans la région française des Pyrénées en exploitant son patrimoine culturel. Il implique des chercheurs de différents domaines : géographes, historiens, anthropologues et informaticiens. Cet article présente deux approches pour tirer parti des points d’intérêts (POI) liés au patrimoine culturel, toutes deux intégrant des concepts du Web de données. La première s’intéresse à la publication automatique des données dans des plateformes open data connues comme Wikipédia. La seconde concerne une application mobile open source dédiée à des itinéraires touristiques. La première approche propose un processus automatique complet permettant de publier n’importe quel jeu de données sur Wikipédia. Grâce à une preuve de concepts (POC), nous avons validé ce processus sur un jeu de données contenant des données géoréférencées du patrimoine culturel, collectées par les chercheurs du projet TCVPYR dans différentes régions des Pyrénées. Ces mêmes données sont également mises à disposition du grand public dans la seconde approche originale de valorisation : une application mobile open source dédiée à la valorisation du patrimoine pyrénéen via la génération personnalisée d’itinéraires touristiques.TCVPYR is a European FEDER project which aims to promote tourism in the French Pyrenees region by leveraging its cultural heritage. It involves scientists from various domains: geographers, historians, anthropologists, and computer scientists. This paper presents two approaches to exploit cultural heritage points of interest (POI), both using Web of data concepts. One consists in publishing these data into the database of well-known open data platforms such as Wikipedia. The other one consists in an open source mobile application dedicated to touristic itineraries. The first approach involves a fully automated process to publish any dataset on Wikipedia. Thanks to a proof of concept (POC), we validate this process on a sample of geo-referenced cultural heritage data collected by TCVPYR researchers in different regions of the Pyrenees. These same data are also highlighted in the second approach via a mobile application recommending personalised touristic itineraries

Travel plan for tourists: minimum access path and route circuit in Jalapão State Park

This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ.This article presents the proposal for a model travel plan for tourists in the Jalapão State Park [PEJ - Parque Estadual do Jalapão], located in the State of Tocantins, Brazil. The research shows the use of the Gurobi Optimizer library in Python Software associated with using Miller-Tucker-Zemlin (MTZ) constraints to ensure a viable route circuit. Through the Traveling Salesman Problem (TSP), two viable optimal routes are presented for two research problems: i) minimize the distance of access to the PEJ from the city of Palmas -TO and ii) find an optimal route path for tourists considering some of the most relevant points of the PEJ. The study presents a viable solution to route problems and contributes with an actual model, showing that TSP and the use of restrictions MTZ can be adequate to solve these problems and others to be solved in PEJ

Benchmarking data mining approaches for traveler segmentation

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers