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Intro; Preface; Organization; Abstracts of Invited Papers; Mapping the Challenges and Opportunities of CBR for eXplainable AI; Some Shades of Grey! Interpretability and Explanatory Capacity of Deep Neural Networks; Model-Based Reasoning for Explainable AI as a Service; Contents; Comparing Similarity Learning with Taxonomies and One-Mode Projection in Context of the FEATURE-TAK Framework; 1 Introduction; 2 Weighted One Mode Projection in FEATURE-TAK; 2.1 FEATURE-TAK; 2.2 Integration of the Weighted One-Mode Projection; 3 Evaluation; 3.1 Similarity Matrix Computation and Modelling Assumptions
3.2 Evaluation Results4 Discussion and Outlook; References; An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs; 1 Introduction; 2 Related Work; 3 Explanations Based on Interaction Graphs; 3.1 The Case-Based Explanation System; 3.2 Link Prediction Similarity Measures; 4 Evaluation; 4.1 Data; 4.2 Experimental Setup; 4.3 Results; 5 Conclusions and Future Work; References; Explanation of Recommenders Using Formal Concept Analysis; 1 Introduction; 2 Related Work; 3 Formal Concept Analysis; 4 FCA-Based Explanation Algorithm
4.1 Explanation of the User Profile4.2 Explaining a Recommendation; 5 Evaluation; 5.1 Global Behaviour of the FCA Lattices; 5.2 Item Selection Strategies; 6 Conclusions and Future Work; References; FLEA-CBR
A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning; 1 Introduction; 2 Related Work; 3 FLEA-CBR; 3.1 Problem Description; 3.2 Overview and Background; 3.3 Core Features; 3.4 Find; 3.5 Learn; 3.6 Explain; 3.7 Adapt; 4 Example Usages; 4.1 CBR and Creativity; 4.2 Library Service Optimization; 5 Conclusion and Future Work; References
Lazy Learned Screening for Efficient Recruitment1 Introduction; 2 Related Work; 2.1 Existing Approaches to Screening; 2.2 Existing Semantic Resources; 3 Design and Implementation; 3.1 Case Representation; 3.2 Similarity Functions; 3.3 The CBR Cycle; 4 Test and Evaluation; 4.1 Setup; 4.2 Experiment 1; 4.3 Experiment 2; 5 Results and Discussion; 5.1 Experiment 1; 5.2 Experiment 2; 6 Conclusion and Future Work; References; On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning; 1 Introduction; 2 Background and Related Work
2.1 Case Base Maintenance and Instance-Based Learning2.2 Sharp and Flat Minima of an Error Function; 3 Case Base Maintenance as Optimization Problem; 3.1 Case Base Editing Problem; 3.2 Introspective Problem-Solving Quality; 3.3 Local Optima in Case Base Editing; 3.4 Hill-Climbing Case Base Editors; 4 Sharpness of a Case Base Configuration; 4.1 Characterizing Flat and Sharp Case Base Editing Optima; 4.2 Discussion of the Sharpness Measure; 5 Empirical Evaluation; 5.1 Correlation Between Sharpness and Generalization; 5.2 Hill-Climber Variants and Their Optima; 6 Conclusion; References
3.2 Evaluation Results4 Discussion and Outlook; References; An Algorithm Independent Case-Based Explanation Approach for Recommender Systems Using Interaction Graphs; 1 Introduction; 2 Related Work; 3 Explanations Based on Interaction Graphs; 3.1 The Case-Based Explanation System; 3.2 Link Prediction Similarity Measures; 4 Evaluation; 4.1 Data; 4.2 Experimental Setup; 4.3 Results; 5 Conclusions and Future Work; References; Explanation of Recommenders Using Formal Concept Analysis; 1 Introduction; 2 Related Work; 3 Formal Concept Analysis; 4 FCA-Based Explanation Algorithm
4.1 Explanation of the User Profile4.2 Explaining a Recommendation; 5 Evaluation; 5.1 Global Behaviour of the FCA Lattices; 5.2 Item Selection Strategies; 6 Conclusions and Future Work; References; FLEA-CBR
A Flexible Alternative to the Classic 4R Cycle of Case-Based Reasoning; 1 Introduction; 2 Related Work; 3 FLEA-CBR; 3.1 Problem Description; 3.2 Overview and Background; 3.3 Core Features; 3.4 Find; 3.5 Learn; 3.6 Explain; 3.7 Adapt; 4 Example Usages; 4.1 CBR and Creativity; 4.2 Library Service Optimization; 5 Conclusion and Future Work; References
Lazy Learned Screening for Efficient Recruitment1 Introduction; 2 Related Work; 2.1 Existing Approaches to Screening; 2.2 Existing Semantic Resources; 3 Design and Implementation; 3.1 Case Representation; 3.2 Similarity Functions; 3.3 The CBR Cycle; 4 Test and Evaluation; 4.1 Setup; 4.2 Experiment 1; 4.3 Experiment 2; 5 Results and Discussion; 5.1 Experiment 1; 5.2 Experiment 2; 6 Conclusion and Future Work; References; On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning; 1 Introduction; 2 Background and Related Work
2.1 Case Base Maintenance and Instance-Based Learning2.2 Sharp and Flat Minima of an Error Function; 3 Case Base Maintenance as Optimization Problem; 3.1 Case Base Editing Problem; 3.2 Introspective Problem-Solving Quality; 3.3 Local Optima in Case Base Editing; 3.4 Hill-Climbing Case Base Editors; 4 Sharpness of a Case Base Configuration; 4.1 Characterizing Flat and Sharp Case Base Editing Optima; 4.2 Discussion of the Sharpness Measure; 5 Empirical Evaluation; 5.1 Correlation Between Sharpness and Generalization; 5.2 Hill-Climber Variants and Their Optima; 6 Conclusion; References