O'Shea, S, Saraee, M and Vadera, S
A web based management of references
, in: The 2004 International Research Conference on Innovations in Information Technology (IIT2004), Dubai, UAE, 2004, Dubai, UAE.
During the evolution of research from the beginning of a project to the end a large amount of information is accumulated from books, journals, articles, manuals and the internet. Managing all this information is a complex and crucial part, especially since it is important to keep track of and acknowledge the works of those who helped to formulate their ideas and also to show that their work is original. Often these references are made available to groups of researchers from websites. Unfortunately on-line bibliography databases often contain hundreds if not thousands of records. When a user visits these sites they are often overwhelmed with inappropriate information. This means Academic Staff have to consciously search through the references. Academic Staff would prefer to have documents and references automatically recommended to them by the application. The main focus of this work is to design and implement a suitable Recommender System to filter and promote the information that users really wanted, helping like researchers to collaborate together more effectively to make a further contribution to world knowledge.
A fully functional reference management system that incorporated both BibTex upload, download, and restricted security facilities has been implemented. By employing recommendation techniques that had been successfully used by commercial and publication websites like Amazon and CiteSeer we designed and implemented several suggestion systems, based on non personalized, and personalized short-term and long term user interests. The Web Based Reference Management System suggests references to the user through the Statistical Summarization, average user (ratings, reviews, and alternate) recommendations. The final recommendation system implemented provides recommendations for references based on a learned user profile, making use of both hotList and coldList user profiles using a form of incremental Hebbian and Anti Hebbian learning rule to incrementally adapt a feature vector providing a fully functional Content Based Filtering System.
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