Research interests arising from photos of nature

These living creatures are really beautiful and amazing. Frankly, I could spend many hours looking at them and reflect on their existence. Their beauty brings not just emotion and memory, but also hope and determination in pursuing the good. To this end, research publications represent only a small contribution

A SENSIBLE ESTIMATED K-NN INQUIRY WITH LOCATION AND INQUIRY SECURITY

Online module concentrates on mining user package and recommending personalized POI sequence according to user package. we advise a Topical Package Model learning approach to instantly mine user travel interest from two social networking, community-contributed photos and travelogues. Travelogue websites offer wealthy descriptions about landmarks and traveling experience compiled by users. We advise Topical Package Model approach to learn user’s and route’s travel attributes. It bridges the space of user interest and routes attributes. We make use of the complementary of two big social networking to create topical package space. We combine user topical interest and also the cost, time, season distribution of every subject to mine user’s consumption capacity, preferred visiting some time and season. After user package mining, we rank famous routes through calculating user package and routes package. Within our paper, we construct the topical package space through the mixture of two social networking: travelogues and community-lead photos. The best column shows the rank of topics while using group of Trip Advisor with corresponding letter a, b, c, d and e. It shows that the data instantly minded is corresponding with human evaluation in the given image albums. To create topical package space, travelogues are utilized to mine representative tags, distribution of cost and visiting duration of each subject, while community-contributed photos are utilized to mine distribution of visiting duration of each subject

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

SENSIBLE ESTIMATED KNN QUERYING FOR SECURE ENQUIRY WITH POSITION

Online segment focus on tunneling user bottle and recommending personalized POI string pursuant to user container. we apprise a Topical Package Model gaining program to instantaneously mine user visit gain from two nice networking, community-contributed engraving and moveogues. Travelogue websites show well-to-do descriptions nearby landmarks and visiting skill compiled by users. We apprise Topical Package Model program to gain user’s and route’s proceed attributes. It bridges the location of user commitment and routes attributes. We employ the reciprocal of two big common networking to start particular box time. We merge user newsworthy commitment and also the cost, time, winter placement of each one contingent on mine user’s decrease facility, picked touring some time and time. After user kit drilling, we rank memorable routes over considerate user container and routes bag. Within our study, we build up the insular bottle distance by the agency of the soup of two common networking: migrateogues and community-lead impression. The best file shows the rank of topics instant employing categorize of Trip Advisor with comparable sign a, b, c, d and e. It shows that the data right away purposing is analogous with child decision in the addicted drawing albums. To plan newsworthy bag field, trekogues are utilized to mine ideal tags, disposal of cost and staying span of each question, period community-contributed engraving are utilized to mine trading of sojourning tide of each idea

Latent Space Model for Multi-Modal Social Data

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.Comment: 12 pages, 7 figures, 2 table

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference

Theory and Applications for Advanced Text Mining

Due to the growth of computer technologies and web technologies, we can easily collect and store large amounts of text data. We can believe that the data include useful knowledge. Text mining techniques have been studied aggressively in order to extract the knowledge from the data since late 1990s. Even if many important techniques have been developed, the text mining research field continues to expand for the needs arising from various application fields. This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. I believe that this book will give new knowledge in the text mining field and help many readers open their new research fields

From the projected to the transmitted image: the 2.0 construction of tourist destination image and identity in Catalonia

This thesis aims to explore online projected and perceived images of a tourist destination, to assess their mutual correspondence, and to shed light on the role of online user-generated images in destination image formation. It also seeks to analyse the spatial distribution of image by tourists and the complex image identity issues concerning a destination. To achieve this, online image sources regarding the case study of Catalonia were analysed through massive computerized quantitative content analysis of some 25,000 travel blog and review entries (perceived image) and around 3,000 official tourism webpages (projected image). The results showed significant dissonance between tourists’ images and official images of the destination in several aspects, notably its attraction factors and cultural identity. Tourists' destination images were found to be greatly concentrated on certain elements and spaces. Finally, this thesis proposes the concept of "transmitted image" to reflect the new central role of tourists’ online images in the creation, dissemination and formation of destination image. Keywords: tourist destination image; destination identity; online image; perceived image; projected image; transmitted image; travel blog; travel review; official tourism websites; Web 2.0; quantitative content analysis; Catalonia.Aquesta tesi té com a objectiu explorar les imatges projectades i percebudes online d’una destinació turística, examinar la seva correspondència mútua, i contribuir a aclarir el rol de les imatges online generades pels usuaris en la formació de la imatge d’una destinació. Amb aquests propòsits, es van analitzar fonts d’imatge online sobre el cas d’estudi de Catalunya a través d’una anàlisi computeritzada quantitativa de contingut massiu d’aproximadament 25.000 entrades de travel blogs i travel reviews (imatge percebuda) i aproximadament 3.000 pàgines de webs oficials (imatge projectada). Els resultats mostren que hi ha una dissonància important entre les imatges dels turistes i les imatges oficials de la destinació en diversos aspectes, notablement en els seus factors d’atracció i identitat cultural. S’ha trobat que les imatges dels turistes sobre la destinació estaven altament concentrades en certs elements i espais. Finalment, aquesta tesi proposa el concepte d’ “imatge transmesa” per tal de reflectir el nou rol central de les imatges online dels turistes en la creació, disseminació i formació de la imatge d’una destinació. Paraules clau: imatge d’una destinació turística; identitat de la destinació; imatge online; imatge percebuda; imatge projectada; imatge transmesa; blog de viatges; review de viatges; webs oficials de turisme; web 2.0; anàlisi de contingut quantitatiu; Catalunya.Esta tesis tiene como objetivo explorar las imágenes proyectadas y percibidas online de un destino turístico, examinar su correspondencia mutua, y contribuir a aclarar el rol de las imágenes online generadas por los usuarios en la formación de la imagen de un destino. Con estos propósitos, se analizaron fuentes de imagen online sobre el caso de estudio de Cataluña a través de un análisis computerizado cuantitativo de contenido masivo de aproximadamente 25.000 entradas de travel blogs y travel reviews (imagen percibida) y aproximadamente 3.000 páginas de webs oficiales (imagen proyectada). Los resultados muestran que hay una disonancia importante entre las imágenes de los turistas y las imágenes oficiales del destino en varios aspectos, notablemente en sus factores de atracción e identidad cultural. Se ha encontrado que las imágenes de los turistas sobre el destino estaban altamente concentradas en ciertos elementos y espacios. Finalmente, esta tesis propone el concepto de “imagen transmitida” para reflejar el nuevo rol central de las imágenes online de los turistas en la creación, diseminación y formación de la imagen de un destino. Palabras clave: imagen de un destino turístico; identidad del destino; imagen online; imagen percibida; imagen proyectada; imagen transmitida; blog de viajes; review de viajes; webs oficiales de turismo; web 2.0; análisis de contenido cuantitativo; Cataluña.Cette thèse a pour objectif d’explorer les images projetées et perçues en ligne d'une destination touristique, examiner sa correspondance mutuelle, et contribuer à clarifier le rôle des images en ligne générées par les usagers dans la formation de l'image d'une destination. Dans ce but, des sources d’image en ligne sur le cas d'étude de la Catalogne ont été analysées à travers d'une analyse informatisée quantitative d'un contenu massif d'à peu près 25.000 travel blogs et travel reviews (image perçue) et à peu près 3.000 pages de sites web officiels (image projetée). Les résultats montrent qu'il y a une dissonance importante entre les images des touristes et les images officielles de la destination selon quelques aspects, surtout dans ses facteurs d'attraction et d'identité culturelle. On a trouvé que les images des touristes sur la destination étaient hautement concentrées dans certains éléments et des espaces. Finalement, cette thèse propose le concept d’ "image transmise" pour refléter le nouveau rôle central des images en ligne des touristes dans la création, la dissémination et la formation de l'image d'une destination. Mots clés: image d'une destination touristique; identité de la destination; image en ligne; image perçue; image projetée; image transmise; blog de voyage; review de voyage; sites web officiels de tourisme; web 2.0; analyse de contenu quantitatif; Catalogne