RACOFI: A Rule-Applying Collaborative Filtering System

In this paper we give an overview of the RACOFI (Rule-Applying Collaborative Filtering) multidimensional rating system and its related technologies. This will be exemplified with RACOFI Music, an implemented collaboration agent that assists on-line users in the rating and recommendation of audio (Learning) Objects. It lets users rate contemporary Canadian music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are then employed together with RuleML-based rules to recommend music objects that best match user queries. RACOFI has been on-line since August 2003 at http://racofi.elg.ca.

Application of Information Retrieval Techniques to Heterogeneous Databases in the Virtual Distributed Laboratory

The Department of Defense (DoD) maintains thousands of Synthetic Aperture Radar (SAR), Infrared (IR), Hyper-Spectral intelligence imagery and Electro-Optical (EO) target signature data. These images are essential to evaluating and testing individual algorithm methodologies and development techniques within the Automatic Target Recognition (ATR) community. The Air Force Research Laboratory Sensors Directorate (AFRL/SN) has proposed the Virtual Distributed Laboratory (VDL) to maintain a central collection of the associated imagery metadata and a query mechanism to retrieve the desired imagery. All imagery metadata is stored in relational database format for access from agencies throughout the federal government and large civilian universities. Each set of imagery is independently maintained at each agency s location along with a local copy of the associated metadata that is periodically updated and sent to the VDL. This research focuses on applying information retrieval techniques to the multiple heterogeneous imagery metadata databases to present users the most relevant images based on user defined search criteria. More specifically, it defines a hierarchical concept thesaurus development methodology to handle the complexities of heterogeneous databases and the application of two classic information retrieval models. The results indicate this type of thesaurus-based approach can significantly increase the precision and recall levels of retrieving relevant documents

Supporting Data mining of large databases by visual feedback queries

In this paper, we describe a query system that provides visual relevance feedback in querying large databases. Our goal is to support the process of data mining by representing as many data items as possible on the display. By arranging and coloring the data items as pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using an interactive query interface, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. Furthermore, by using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, may easier understand the overall result. Our system allows to represent the largest amount of data that can be visualized on current display technology, provides valuable feedback in querying the database, and allows the user to find results which, otherwise, would remain hidden in the database

A design of Personal Information Push-Delivery System on the Internet

The Internet provides a powerful disseminative ability for users to acquire information more efficiently and quickly. However, an increasingly large scale of data induces certain problems as users face a more serious information overload situation. By using an information retrieval technique, information push-delivery provides a good solution for users to acquire rich information from the Internet. In fact, providing personal service for users is one of the mo st important issues in an electronic commerce (EC) environment. In order to increase interaction between themselves and customers, many enterprises provide personal services to improve management performance and competitiveness. However, since the customers have different preferences for information received from the Internet, it seems necessary to design a personal information system to guarantee that the customers can receive the desired information. In this study, the fuzzy retrieval and similarity measurement techniques are applied to design a personal information push-delivery system. The data resulting from testing a group of students at Da-Yeh University, Changhua, Taiwan, shows that the satisfaction degree for the received information for all participants was 70%. These results indicate that the proposed system can effectively provide correct and interesting information to users

Recommendation Systems for Online Learning Materials Using Cosine Similarity and Simple Additive Weighting

This study focuses on searching for teaching materials in order to obtain relevant teaching material information appropriately for further use as material for recommendation of course material to students using the Cosine Similarity method and calculating weighting using the Simple Additive Weighting (SAW) method. With the SAW method, 3 criteria and weight values are determined for each attribute, followed by a ranking process. So that in the end the search results that are ranked in the order of similarity and most relevant can be displayed and then selected and used as recommendations in the student's e-learning learning system. From the results of the study, the Cosine Similarity and SAW methods have provided a fairly good/effective recommendation with an average precision of 0.7867 and a recall of 0.766 so that this method is appropriate to be placed in campus e-Learnin