Inhalt

•  
  Introduction
•  
   Motivation
•  
   Introductory Example
•  
   Goals
•  
   Thesis Structure
•  
  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
•  
   Accuracy Metrics
•  
   Non-Accuracy Metrics
•  
   Performance Metrics
•  
   Evaluating Recommender Systems
•  
   Offline Evaluation
•  
   Online Evaluation
•  
  Evaluation Libraries and Frameworks
•  
   Libraries
•  
   Frameworks
•  
   Recommender System Services
•  
  Industrial Recommender System Implementations
•  
   Netflix Recommender System
•  
   Mendeley Suggest Architecture
•  
   Zalando Recommender System
•  
   Summary
•  
   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
•  
   Types
•  
   Consortia
•  
   Requirements on Benchmarks
•  
  State-of-the-Art Benchmarks
•  
   TPC-DS
•  
   BigBench
•  
   Graphalytics
•  
   BigDataBench
•  
   acr:szts
•  
   SparkBench
•  
   StreamBench
•  
   acr:ycsb
•  
   MLPerf
•  
   Summary
•  
  Benchmark Model and Benchmark Execution Process
•  
   Benchmark Model
•  
   Benchmark Execution Process
•  
  A Benchmark Concept for Recommender Systems based on Omni-Channel Data
•  
   Channels and Signal Types
•  
   Data Model
•  
  Data
•  
   Own and Public Data
•  
   Data Generation
•  
  Data Processing
•  
   User Matching
•  
   Item Matching
•  
   Content Matching
•  
  Data Aggregation
•  
   Binary Aggregation
•  
   Equally Weighted Aggregation
•  
   Weighted Aggregation
•  
   Sequence-based Aggregation
•  
   Generalized Aggregation
•  
   Benchmarking Process
•  
   Overview
•  
   Data Loading
•  
   Model Training
•  
   Model Testing
•  
   Benchmark Components
•  
  Implementation of the Recommender System Benchmark
•  
   Overview
•  
   Modules
•  
   Data Loading
•  
   Data Preprocessing
•  
   Data Aggregation
•  
   Data Splitting
•  
   Algorithms
•  
   Evaluation
•  
   Configuration
•  
   Visualization
•  
  Application of the Recommender System Benchmark
•  
  Analysis of the Online Retailer Data
•  
   Collaboration Context
•  
   Channels and Signal Types
•  
   Data Analysis
•  
   Data Preprocessing
•  
   Considered Data
•  
  Application of the Benchmark to the Online Retailer Data
•  
   General Setup
•  
   Binary Aggregation on Purchase Data
•  
   Binary Aggregation on Omni-Channel Data
•  
   Weighting-based Aggregation on Omni-Channel Data
•  
   Sequence-based Aggregation on Omni-Channel Data
•  
   Overall Analysis
•  
   Discussion
•  
  Conclusion
•  
   Summary
•  
   Outlook
•  
   Bibliography
•  
   List of Web Pages