2,924 research outputs found

    Trip Prediction by Leveraging Trip Histories from Neighboring Users

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    We propose a novel approach for trip prediction by analyzing user's trip histories. We augment users' (self-) trip histories by adding 'similar' trips from other users, which could be informative and useful for predicting future trips for a given user. This also helps to cope with noisy or sparse trip histories, where the self-history by itself does not provide a reliable prediction of future trips. We show empirical evidence that by enriching the users' trip histories with additional trips, one can improve the prediction error by 15%-40%, evaluated on multiple subsets of the Nancy2012 dataset. This real-world dataset is collected from public transportation ticket validations in the city of Nancy, France. Our prediction tool is a central component of a trip simulator system designed to analyze the functionality of public transportation in the city of Nancy

    Personalised Context Aware Content Relevant Disease Prediction And Diet Recommendation System

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    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

    Traveltant: Social Interaction Based Personalized Recommendation System

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    Trip planning is a time consuming task that most people do before going to any destination. Traveltant is an intelligent system that analyzes a user\u27s Social network and suggests a complete trip plan detailed for every single day based on the user\u27s interests extracted from the Social network. Traveltant also considers the interests of friends the user interacts with most by building a ranked friends list of interactivity, and then uses the interests of those people in this list to enrich the recommendation results. Traveltant provides a smooth user interface through a Windows Phone 7 application while doing most of the work in a backend cloud service. To evaluate the results of the system, volunteers have rated the personalized results better than those results from only common factors such popularity and rating

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

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    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

    SECURED LOCATION AWARE QUERIES WITH SENSIBLE KEYWORDS

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    Online measure observes tunneling user container and recommending personalized POI array just as user box. We caution a Topical Package Model gaining way to now mine user move earnings from two common networking, community-contributed impression and vestiges. Travelogue websites show affluent descriptions almost landmarks and proceeding reality compiled by users. We tell Topical Package Model program to hear users and route’s trek attributes. It bridges the field of user gain and routes attributes. We abuse the reciprocal of two big societal networking to forge insular box slot. We link user parochial commitment and the cost, time, winter trading of without exception contingent on mine user’s decrease talent, adopted sojourning some time and winter. After user container digging, we rank memorable routes by the agency of canny user kit and routes container. Within our script, we found the newsworthy container location over the soup of two nice networking: moveouts and community-lead statue. The best file shows the rank of topics moment accepting troop of Trip Advisor with reciprocal report a, b, c, d and e. It shows that the data directly purposing is comparable with opinion in the inured icon albums. To forge parochial bag location, trilogies are utilized to mine ideal tags, disposal of cost and stopping extent of each question, period community-contributed engraving are utilized to mine disposal of touring continuation of each idea

    The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

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    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

    Travel Recommendation via Author Topic Model Based Collaborative Filtering

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    Ministry of Education, Singapore under its Academic Research Funding Tier

    A COMPARATIVE STUDY ON HEART DISEASE ANALYSIS USING CLASSIFICATION TECHNIQUES

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    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

    Investigation of Effective Classification Method for Online Health Service Recommendation System

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    Hospital Recommendation Services have been gaining popularity these days. There are many applications and systems that are recommending hospitals based on the user’s requirements and to meet the patient satisfaction. These applications take the reviews of the patients and the users and based on these reviews, they recommend the hospitals. Also if a person is new to the location that he is currently residing, when the speciality is given as input by him, then these applications recommend the hospitals. But the problem is that everyone is not aware of the medical terms like specialities. For those people, “Health Service Recommendation System” comes handy. “Health Service Recommendation System” is an Android Application for finding hospitals within a specified range of distance and requirements provided by the client using the Naïve Bayes classification algorithm. Naïve Bayes algorithm classifies the speciality and thus helps in achieving the maximum accuracy compared to the other algorithms used. This application is helpful even for the people who are not aware of the specialities of the hospitals
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