Mehmet Göker

Case-based recommender systems

We describe recommender systems and especially case-based recommender systems. We define a framework in which these systems can be understood. The framework contrasts collaborative with case-based, reactive with proactive, single-shot with conversational, and asking with proposing. Within this framework, we review a selection of papers from the case-based recommender systems literature, covering the development of these systems over the last ten years.

Designing Industrial Case-Based Reasoning Applications

The development of knowledge management applications for business environments requires balancing the needs of various parties within the client’s organization. While the end users desire to have a system that operates as effectively and efficiently as possible without changing their existing workflow, the business unit may be interested in capturing, verifying and endorsing corporate policies. The IT department of the client will be interested in applications that fit into their standard deployment environment and will certainly not appreciate the need for the business unit to modify some of the knowledge containers occasionally. Balancing these requirements without endangering the long term success of an application can become challenging.

Recommender Systems: An Overview

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of this special issue is to take stock of the current landscape of recommender systems research and identify directions the field is now taking. This article provides an overview of the current state of the field and introduces the various articles in the special issue.

Case-based reasoning for diagnosis applications

Case-based reasoning (CBR) is widely applicable to the diagnosis of problems and the identification of solutions to them. This review of the literature identifies key papers relating to this use of CBR. The stages in diagnosing and troubleshooting a problem are considered and the elements of a CBR system for achieving this are described. Five systems are discussed that have frequently been cited in the literature and illustrate the use of CBR in diagnosis and troubleshooting applications.

Advancing AI Research and Applications by Learning from What Went Wrong and Why

This special issue of AI Magazine is dedicated to the proposition that problems populate the path to insight, implying the experiences and lessons learned should be shared.

The pwc connection machine: an adaptive expertise provider

The Connection Machine helps PricewaterhouseCoopers LLP (PwC) partners and staff to solve problems by connecting people to people. It allows information seekers to enter their question in free text, finds knowledgeable colleagues, forwards the question to them, obtains the answer and sends it back to the seeker. In the course of this interaction, the application unobtrusively learns and updates user profiles and thereby increases its routing accuracy. The Connection Machine combines features of expertise locators, adaptive case-based recommender systems and question answering applications. This document describes the core technology that supports the workflow, the user modeling and the retrieval technology of the Connection Machine.

Selected Applications of the Structural Case-Based Reasoning Approach

In this chapter we describe four applications that have been developed using the structural CBR approach and the INRECA methodology: The case-based help-desk support system HOMER, the Analog Devices’ product catalog, the case-based maintenance application for the high-speed train TGV, and the Fraunhofer IESE experience factory. The four systems represent common application areas for CBR techniques. They are distinct in terms of their complexity, the representation and similarity measures that are needed to develop the systems, and the processes that have to be put into place to operate the applications. Hence, they illustrate the versatility of the INRECA approach and the bandwidth of solutions that can be developed with it.

About this Author

With this book you can learn a proven method for solving business problems. And you’ll come to understand why this methodology is the right solution for you. It addresses issues that are important for both software managers and technical staff. These issues will be particularly useful before the start or the set-up of an investment in building a case-based application.

Case-Based Reasoning Approaches

Case-based reasoning means learning from previous experiences. Given the fact that this is a very general approach to human problem-solving behavior, it is more than natural that there are different approaches for implementing this process on computer systems. In commercial CBR systems, there are three main approaches that differ in the sources, materials, and knowledge they use.

AAAI 2008 Workshop Reports

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13–14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers; AI Education Workshop Colloquium; Coordination, Organizations, Institutions, and Norms in Agent Systems, Enhanced Messaging; Human Implications of Human-Robot Interaction; Intelligent Techniques for Web Personalization and Recommender Systems; Metareasoning: Thinking about Thinking; Multidisciplinary Workshop on Advances in Preference Handling; Search in Artificial Intelligence and Robotics; Spatial and Temporal Reasoning; Trading Agent Design and Analysis; Transfer Learning for Complex Tasks; What Went Wrong and Why: Lessons from AI Research and Applications; and Wikipedia and Artificial Intelligence: An Evolving Synergy.