Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

DeepStore: an interaction-aware Wide&Deep model for store site recommendation with attentional spatial embeddings

International audienceStore site recommendation is one of the essential business services in smart cities for brick-and-mortar enterprises. In recent years, the proliferation of multisource data in cities has fostered unprecedented opportunities to the data-driven store site recommendation, which aims at leveraging large-scale user-generated data to analyze and mine users' preferences for identifying the optimal location for a new store. However, most works in store site recommendation pay more attention to a single data source which lacks some significant data (e.g., consumption data and user profile data). In this paper, we aim to study the store site recommendation in a fine-grained manner. Specifically, we predict the consumption level of different users at the store based on multisource data, which can not only help the store placement but also benefit analyzing customer behavior in the store at different time periods. To solve this problem, we design a novel model based on the deep neural network, named DeepStore, which learns low-and high-order feature interactions explicitly and implicitly from dense and sparse features simultaneously. In particular, DeepStore incorporates three modules: 1) the cross network; 2) the deep network; and 3) the linear component. In addition, to learn the latent feature representation from multisource data, we propose two embedding methods for different types of data: 1) the filed embedding and 2) attention-based spatial embedding. Extensive experiments are conducted on a real-world dataset including store data, user data, and point-of-interest data, the results demonstrate that DeepStore outperforms the state-of-the-art models

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Using Semantic-Based User Profile Modeling for Context-Aware Personalised Place Recommendations

Place Recommendation Systems (PRS's) are used to recommend places to visit to World Wide Web users. Existing PRS's are still limited by several problems, some of which are the problem of recommending similar set of places to different users (Lack of Personalization) and no diversity in the set of recommended items (Content Overspecialization). One of the main objectives in the PRS's or Contextual suggestion systems is to fill the semantic gap among the queries and suggestions and going beyond keywords matching. To address these issues, in this study we attempt to build a personalized context-aware place recommender system using semantic-based user profile modeling to address the limitations of current user profile building techniques and to improve the retrieval performance of personalized place recommender system. This approach consists of building a place ontology based on the Open Directory Project (ODP), a hierarchical ontology scheme for organizing websites. We model a semantic user profile from the place concepts extracted from place ontology and weighted according to their semantic relatedness to user interests. The semantic user profile is then exploited to devise a personalized recommendation by re-ranking process of initial search results for improving retrieval performance. We evaluate this approach on dataset obtained using Google Paces API. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based place recommendation model

The Impact of Information and Communication Technology on the Tourism Sector

Information and Communication Technology (ICT) has changed the global businesses environment by a wide range of tools, methodologies and functions, facilitating the strategic management and supporting firms to achieve a long term competitive advantage. The aim of this paper is to provide an overview of the new applications of Information Communication Technology in tourism industry, the contribution of ICT to the promotion of the tourist product, as well as the potential to the tourism management and the process of decision-making. One important tool, which helps in making decisions in the field of tourism economy, is the Geographic Information System (GIS), which provides a comprehensible representation of the statistical figures of the tourism economy by facilitating decision-making on tourism policy. In this paper is presented some tourist financial figures and their visualization through graphs by Geographic Information System