Homann, Leschek Adam; Fitzek, Leschek Adam: Benchmarking recommender systems. 2020
Inhalt
- Introduction
- Recommender Systems
- Motivation
- Application Domains
- Explicit and Implicit Feedback
- Approaches
- Collaborative Filtering Approaches
- Content-Based Filtering Approaches
- Hybrid Approaches
- Selected Recommender System Research
- Recommender Systems as Machine Learning Systems
- Metrics
- Evaluating Recommender Systems
- Evaluation Libraries and Frameworks
- Industrial Recommender System Implementations
- General Trends and Future Developments
- Discussion
- Fundamentals of Benchmarking
- Motivation
- History of Benchmarking
- Benchmarking Origin
- Benchmarking Information Technologies
- Benchmarking Database and Big Data Systems
- Benchmarking Types and Consortia
- Requirements on Benchmarks
- State-of-the-Art Benchmarks
- Benchmark Model and Benchmark Execution Process
- A Benchmark Concept for Recommender Systems based on Omni-Channel Data
- Channels and Signal Types
- Data Model
- Data
- Data Processing
- Data Aggregation
- Binary Aggregation
- Equally Weighted Aggregation
- Weighted Aggregation
- Sequence-based Aggregation
- Generalized Aggregation
- Benchmarking Process
- Implementation of the Recommender System Benchmark
- Application of the Recommender System Benchmark
- Conclusion
- Bibliography
- List of Web Pages
