9,018 research outputs found
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
Personalised Context Aware Content Relevant Disease Prediction And Diet Recommendation System
Predicting disease is plays an important role in improving the public health. The problem of predicting possible diseases reduce some of the diseases may come in the future. The health recommendation system predicts the disease and recommends the suitable diet and exercises. Context aware recommendation systems produce more relevant recommendations with the help of contextual information. In this paper we have proposed a context aware recommendation system to predict diseases based in the context of the user and recommend a suitable diet and exercises. The experimental results show that the performance of our proposed system is an efficient in predicting the disease and recommending the diet and exercise
Development of Context-Aware Recommenders of Sequences of Touristic Activities
En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lÃcules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turÃstiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turÃstics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turÃstics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turÃstiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i perÃodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implÃcites entre els punts d'interès de cada perfil. Finalment, es fa un rà nquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turÃstica. També realitzem una segona fase d'anà lisi dels fluxos turÃstics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turÃstica. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turÃstica.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de pelÃculas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turÃsticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turÃsticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turÃsticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turÃsticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minerÃa de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turÃstica. También llevamos a cabo un análisis de los flujos turÃsticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turÃstica. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turÃstica.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations
seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES
As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms
A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES
As it is modern era where people use computers more for work and other purposes physical activities are reduced. Due to work pressure they are not worrying about food habits. This results in introduction of junk food. These junk foods in turn results in many health issues. Major issue is heart disease. It is the major cause of casualty all over the world. Prediction of such heart disease is a tough task. But Countless mining approaches overcome this difficulty. Nowadays data mining techniques play’s an important role in many fields such as business application, stock market analysis, e-commerce, medical field and many more. Previously many techniques like Bayesian classification, decision tree and many more are employed for heart disease prediction. In this proposal we are going to do a comparative study on three algorithms
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