Mehmet H. Göker

A personalized system for conversational recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system - the ADAPTIVE PLACE ADVISOR - that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

Personalized Conversational Case-Based Recommendation

In this paper, we describe the Adaptive Place Advisor, a user adaptive, conversational recommendation system designed to help users decide on a destination, specifically a restaurant. We view the selection of destinations as an interactive, conversational process, with the advisory system inquiring about desired item characteristics and the human responding. The user model, which contains preferences regarding items, attributes, values, value combinations, and diversification, is also acquired during the conversation. The system enhances the users requirements with the user model and retrieves suitable items from a case-base. If the number of items found by the system is unsuitable (too high, too low) the next attribute to be constrained or relaxed is selected based on the information gain associated with the attributes. We also describe the current status of the system and future work.

The development and utilization of the case-based help-desk support system HOMER

Abstract Current case-based reasoning (CBR) process models present CBR as a low-maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, utilization and maintenance have to be put in place. In this paper the shortcomings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems are described.

The Development of HOMER: A Case-Based CAD/CAM Help-Desk Support Tool

The increasing number of hardware and software at Daimler-Benz personal car development in Sindelfingen combined with the constant number of help-desk operators demanded a help-desk system which goes beyond the classical trouble-ticket approach. In this application paper we give an overview of the situation at the CAD/CAM Help-Desk in Sindelfingen and the development of the case-based help-desk support tool HOMER. We describe our modeling approach and its influence on the system architecture as well as the different user roles and the help-desk tool itself. We conclude with the lessons learned during the course of this project and future prospects.

Development and Utilization of a Case-Based Help-Desk Support System in a Corporate Environment

Current Case-Based Reasoning (CBR) process models present CBR as a low maintenance AI-technology and do not take the processes that have to be enacted during system development and utilization into account. Since a CBR system can only be useful if it is integrated into an organizational structure and used by more than one user, processes for continuous knowledge acquisition, -utilization and -maintenance have to be put in place. In this paper the short-comings of classical CBR process models are analyzed, and, based on the experiences made during the development of the case-based help-desk support system HOMER, the managerial, organizational and technical processes related to the development and utilization of CBR systems described.

Collecting Experience on the Systematic Development of CBR Applications Using the INRECA Methodology

This paper presents an overview of the INRECA methodology for building and maintaining CBR applications. This methodology supports the collection and reuse of experience on the systematic development of CBR applications. It is based on the experience factory and the software process modeling approach from software engineering. CBR development experience is documented using software process models and stored in different levels of generality in a three-layered experience base. Up to now, experience from 9 industrial projects enacted by all INRECA II partners has been collected.

Development of Industrial Knowledge Management Applications with Case-Based Reasoning

The successful development, deployment and utilization of Case-Based Reasoning Systems in commercial environments require the development team to focus on aspects that go beyond the core CBR engine itself. Characteristics of the Users, the Organization and the Domain have considerable impact on the design decisions during implementation and on the success of the project after deployment. If the system is not technically and organizationally integrated with the operating environment, it will eventually fail. In this chapter, we describe our experiences and the steps we found useful while implementing CBR applications for commercial use. We learned these lessons the hard way. Our goal is to document our experience and help practitioners develop their own approach and avoid making the same mistakes.

Advances in Case-Based Reasoning

Invited Talks.- The Fun Begins with Retrieval: Explanation and CBR.- Completeness Criteria for Retrieval in Recommender Systems.- Is Consideration of Background Knowledge in Data Driven Solutions Possible at All?.- Reality Meets Research.- Research Papers.- Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners.- Retrieving and Reusing Game Plays for Robot Soccer.- Self-organising Hierarchical Retrieval in a Case-Agent System.- COBRAS: Cooperative CBR System for Bibliographical Reference Recommendation.- A Knowledge-Light Approach to Regression Using Case-Based Reasoning.- Case-Base Maintenance for CCBR-Based Process Evolution.- Evaluating CBR Systems Using Different Data Sources: A Case Study.- Decision Diagrams: Fast and Flexible Support for Case Retrieval and Recommendation.- Case-Based Reasoning for Knowledge-Intensive Template Selection During Text Generation.- Rough Set Feature Selection Algorithms for Textual Case-Based Classification.- Experience Management with Case-Based Assistant Systems.- The Needs of the Many: A Case-Based Group Recommender System.- Contextualised Ambient Intelligence Through Case-Based Reasoning.- Improving Annotation in the Semantic Web and Case Authoring in Textual CBR.- Unsupervised Case Memory Organization: Analysing Computational Time and Soft Computing Capabilities.- Further Experiments in Case-Based Collaborative Web Search.- Finding Similar Deductive Consequences - A New Search-Based Framework for Unified Reasoning from Cases and General Knowledge.- Case-Based Sequential Ordering of Songs for Playlist Recommendation.- A Comparative Study of Catalogue-Based Classification.- Ontology-Driven Development of Conversational CBR Systems.- Complexity Profiling for Informed Case-Base Editing.- Unsupervised Feature Selection for Text Data.- Combining Case-Based and Similarity-Based Product Recommendation.- On the Use of Selective Ensembles for Relevance Classification in Case-Based Web Search.- What Evaluation Criteria Are Right for CCBR? Considering Rank Quality.- Fast Case Retrieval Nets for Textual Data.- Combining Multiple Similarity Metrics Using a Multicriteria Approach.- Case Factory - Maintaining Experience to Learn.- Retrieval over Conceptual Structures.- An Analysis on Transformational Analogy: General Framework and Complexity.- Discovering Knowledge About Key Sequences for Indexing Time Series Cases in Medical Applications.- Application Papers.- Case-Based Reasoning for Autonomous Service Failure Diagnosis and Remediation in Software Systems.- Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System.- Case-Based Support for Collaborative Business.- A CBR-Based Approach for Supporting Consulting Agencies in Successfully Accompanying a Customers Introduction of Knowledge Management.- The PwC Connection Machine: An Adaptive Expertise Provider.