Interacting and making personalized recommendations of places of interest to tourists

Advances in Intelligent Systems and Computing, 353Nowadays, applications that are developed to support tourists should go much further than simply providing information about places or recommending places or routes based on the user location. They should be able to provide users with simple mechanisms to interact with places of interest and provide them with relevant information and recommendations about new relevant places of interest or tours according to their preferences and the preferences of other tourists with similar interests. In this work we describe a system that explores information about tourists’ interactions with places of interest and their opinions about each place, to recommend new places of interest, pedestrian tours and to promote products and services which are in accordance with their expectations. First experiments show that the system can help the tourists to interact with places of interest, helping them in their visits and also to promote shops and services

Topic mining of tourist attractions based on a seasonal context aware LDA model

With the rise of personalized travel recommendation in recent years, automatic analysis and summary of the tourist attraction is of great importance in decision making for both tourists and tour operators. To this end, many probabilistic topic models have been proposed for feature extraction of tourist attraction. However, existing state-of-the-art probabilistic topic models overlook the fact that tourist attractions tend to have distinct characteristics with respect to specific seasonal context. In this article, we contribute the innovative idea of using seasonal contextual information to refine the characteristics of tourist attractions. Along this line, we first propose STLDA, a season topic model based on latent Dirichlet allocation which can capture meaningful topics corresponding to various seasonal contexts for each attraction. Then, an inference algorithm using Gibbs sampling is put forward to learn the model parameters of our proposed model. In order to verify the effectiveness of STLDA model, we present a detailed experimental study using collected real-world textual data of tourist attractions. The experimental analysis results show that the superiority of STLDA over the basic LDA model in providing a representative and comprehensive summarization related to each tourist attraction. More importantly, it has great significance for improving the level of personalized attraction recommendation

Socio-geography of human mobility: a study using longitudinal mobile phone data

A relationship between people’s mobility and their social networks is presented based on an analysis of calling and mobility traces for one year of anonymized call detail records of over one million mobile phone users in Portugal. We find that about 80% of places visited are within just 20 km of their nearest (geographical) social ties’ locations. This figure rises to 90% at a ‘geo-social radius’ of 45 km. In terms of their travel scope, people are geographically closer to their weak ties than strong ties. Specifically, they are 15% more likely to be at some distance away from their weak ties than strong ties. The likelihood of being at some distance from social ties increases with the population density, and the rates of increase are higher for shorter geo-social radii. In addition, we find that area population density is indicative of geo-social radius where denser areas imply shorter radii. For example, in urban areas such as Lisbon and Porto, the geo-social radius is approximately 7 km and this increases to approximately 15 km for less densely populated areas such as Parades and Santa Maria da Feira

観光スポットとルート推薦のためのユーザ適応型旅行プラン生成アルゴリズム

早大学位記番号:新8428早稲田大

Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.Comment: 12 page

Understanding Social Characteristic from Spatial Proximity in Mobile Social Network

Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset

Images of Dutchness: Popular Visual Culture, Early Cinema and the Emergence of a National Cliché

Why do early films present the Netherlands as a country full of canals and windmills, where people wear traditional costumes and wooden shoes, while industries and modern urban life are all but absent? Where do such visual clichés come from? This study investigates the roots of this imagery in popular visual media ranging from magazines to tourist brochures, from anthropological treatises to advertising trade cards, stereoscopic photographs, picture postcards, magic lantern slide sets and films of early cinema. The book provides an in-depth study of this rich and fascinating corpus of popular visual media that has not been studied before, and the discourses that these images were meant to illustrate. This intermedial approach offers new insights into the emergence of national clichés and the study of stereotypical thinking

Filling the Gaps: On the Completion of Sparse Call Detail Records for Mobility Analysis

International audienceCall Detail Records (CDRs) have been widely used in the last decades for studying different aspects of human mobility. The accuracy of CDRs strongly depends on the user-network interaction frequency: hence, the temporal and spatial spar-sity that typically characterize CDR can introduce a bias in the mobility analysis. In this paper, we evaluate the bias induced by the use of CDRs for inferring important locations of mobile subscribers, as well as their complete trajectories. Besides, we propose a novel technique for estimating real human trajectories from sparse CDRs. Compared to previous solutions in the literature, our proposed technique reduces the error between real and estimated human trajectories and at the same time shortens the temporal period where users' locations remain undefined