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Full-Text Articles in Physical Sciences and Mathematics
Conceptual, Impact-Based Publications Recommendations, Ann Smittu Joseph
Conceptual, Impact-Based Publications Recommendations, Ann Smittu Joseph
Graduate Theses and Dissertations
CiteSeerx is a digital library for scientific publications by computer science researchers. It also functions as a search engine with several features including autonomous citation indexing, automatic metadata extraction, full-text indexing and reference linking. Users are able to retrieve relevant documents from the CiteSeerx database directly using search queries and will further benefit if the system suggests document recommendations to the user based on their preferences and search history. Therefore, recommender systems were initially developed and continue to evolve to recommend more relevant documents to the CiteSeerx users. In this thesis, we introduce the Conceptual, Impact-Based Recommender (CIBR), …
A Bandwidth-Conserving Architecture For Crawling Virtual Worlds, Dipesh Gautam
A Bandwidth-Conserving Architecture For Crawling Virtual Worlds, Dipesh Gautam
Graduate Theses and Dissertations
A virtual world is a computer-based simulated environment intended for its users to inhabit via avatars. Content in virtual worlds such as Second Life or OpenSimulator is increasingly presented using three-dimensional (3D) dynamic presentation technologies that challenge traditional search technologies. As 3D environments become both more prevalent and more fragmented, the need for a data crawler and distributed search service will continue to grow. By increasing the visibility of content across virtual world servers in order to better collect and integrate the 3D data we can also improve the crawling and searching efficiency and accuracy by avoiding crawling unchanged regions …
Traveltant: Social Interaction Based Personalized Recommendation System, Sultan Dawood Alfarhood
Traveltant: Social Interaction Based Personalized Recommendation System, Sultan Dawood Alfarhood
Graduate Theses and Dissertations
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's Social network and suggests a complete trip plan detailed for every single day based on the user's 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 …
Identifying Robust Sift Features For Improved Image Alignment, Sanjay Abhinav Vemuri
Identifying Robust Sift Features For Improved Image Alignment, Sanjay Abhinav Vemuri
Graduate Theses and Dissertations
In this thesis, we will study different ways to improve feature matching by increasing the quality and reducing the number of SIFT features. We created an algorithm to identify robust SIFT features by evaluating how invariant individual feature points are to changes in scale. This allows us to exclude poor SIFT feature points from the matching process and obtain better matching results in reduced time. We also developed techniques consider scale ratios and changes in object orientation when performing feature matching. This allows us to exclude false-positive feature matches and obtain better image alignment results.
Personalized News Recommender Using Twitter, Satya Srinivasa Nirmal Jonnalagedda
Personalized News Recommender Using Twitter, Satya Srinivasa Nirmal Jonnalagedda
Graduate Theses and Dissertations
Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily swamped by information of little interest to them. News recommender systems are one approach to help users find interesting articles to read. News recommender systems present the articles to individual users based on their interests rather than presenting articles in order of their occurrence. In this thesis, we present our research on developing personalized news recommendation system with the help of a popular micro-blogging service "Twitter". The news articles are …