Design and implementation of a filter engine for semantic web documents

This report describes our project that addresses the challenge of changes in the semantic web. Some studies have already been done for the so-called adaptive semantic web, such as applying inferring rules. In this study, we apply the technology of Event Notification System (ENS). Treating changes as events, we developed a notification system for such events

Design and Implementation of a Customer Personalised Recomender System

[ANGLÈS] Market basket analysis is examined through the application of probabilistic topic models and case-based reasoning in order to provide more insight into customer buying habits and generate meaningful recommendations

A Case-Based Reasoning View of Automated Collaborative Filtering

From some perspectives Automated Collaborative Filtering (ACF) appears quite similar to Case-Based Reasoning (CBR). It works on data organised around users and assets that might be considered case descriptions. In addition, in some versions of ACF, much of the induction is deferred to run time -- in the lazy learning spirit of CBR. On the other hand, because of its lack of semantic descriptions it seems to be the antithesis of case-based reasoning -- a learning approach based on case representations. This paper analyses the characteristics shared by ACF and CBR, it highlights the differences between the two approaches and attempts to answer the question "When is it useful or valid to consider ACF as CBR?". We argue that a CBR perspective on ACF can only be useful if it offers insights into the ACF process and supports a transfer of techniques. In conclusion we present a case retrieval net model of ACF and show how it allows for enhancements to the basic ACF idea