Printer troubleshooting using Bayesian networks
IEA/AIE '00 Proceedings of the 13th international conference on Industrial and engineering applications of artificial intelligence and expert systems: Intelligent problem solving: methodologies and approaches
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
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Internet shopping using some on-line catalogue of products is one of the fastest growing businesses now. An intelligent support for selecting a product in such a catalogue should work as an assistant (on request), not forcing the user to perform any unnecessary steps. As many Internet portals which support browsing and search, the proposed solution should support browsing (the catalogue) and recommending. Our paper describes some ongoing work on intelligent shopping assistant of an on-line catalogue of a hardware vendor who sells a relatively restricted scale of products (PC's) manufactured by a single company. We propose a solution that will make browsing the catalogue easier, will recommend products, and will adapt its behavior to different types of users. Grouping the products into easily interpretable clusters can enhance browsing. To recommend a product and to adapt the system to different users, we propose to use a Bayesian network. We believe that our solution will be able to automatically follow the changes in the notions of computer characteristics (as the performance of new products increases), will be able to easy incorporate new products and will be able to understand requests from different types of users.