Contents 1 Introduction to Recommender Systems Handbook francesco Ricci, Lior Rokach and Bracha Shapira 1.1 Introduction 1. 2 Recommender Systems Function 4 1.3 Data and Knowledge sources 1.4 Recommendation Techniques 1.5 Application and Evaluation 1.6 Recommender Systems and Human Computer Interaction 1.6.1 Trust, Explanations and Persuasiveness 0478 1.6.2 Conversational Systems 1.6.3 Visualization 1.7 Recommender Systems as a Multi-Disciplinary Field Emerg 1.8.1 Emerging Topics Discussed in the Handbook 18. 2 Challenges References Part I Basic Techniques 2 Data Mining Methods for Recommender Systems Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M. Puj 2.1 Introduction 2.2 Data Preprocessing 2.2.1 Similarity Measures 2.2.3 Reducing Dimensionality 2.2.4 Denoiser 3 Classification 2.3. 1 Nearest Neighbors ecision irees 2.3.3 Ruled-based ClassifiersContents 1 Introduction to Recommender Systems Handbook . . . . . . . . . . . . . . . . 1 Francesco Ricci, Lior Rokach and Bracha Shapira 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Recommender Systems Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Data and Knowledge Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 Recommendation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.5 Application and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1.6 Recommender Systems and Human Computer Interaction . . . . . . . 17 1.6.1 Trust, Explanations and Persuasiveness . . . . . . . . . . . . . . . 18 1.6.2 Conversational Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.6.3 Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.7 Recommender Systems as a Multi-Disciplinary Field . . . . . . . . . . . 21 1.8 Emerging Topics and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.8.1 Emerging Topics Discussed in the Handbook . . . . . . . . . . 23 1.8.2 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Part I Basic Techniques 2 Data Mining Methods for Recommender Systems . . . . . . . . . . . . . . . . 39 Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M. Pujol 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.2 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.2.1 Similarity Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 2.2.2 Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.2.3 Reducing Dimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.2.4 Denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2.3 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.3.1 Nearest Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.3.2 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.3.3 Ruled-based Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 ix