Contents
Contents 4
List of Symbols 7
1 Introduction 9
1.1 Semi-supervised learning on networks . . . . 10
1.2 Collaborative filtering . . . . 11
1.3 Connecting the dots . . . . 11
1.4 Structure of the thesis . . . . 12
1.5 List of publications . . . . 13
I Semi-supervised learning on networks 15 2 Introduction 17 2.1 Machine learning problems on networks . . . . 18
2.2 Similarity measures . . . . 21
2.3 Methods for semi-supervised learning . . . . 23
2.4 Challenges and contributions . . . . 27
3 Semi-supervised learning with path-based modularity max- imisation 29 3.1 Introduction . . . . 29
3.2 Background on the modularity . . . . 30
3.3 Random Walk based Modularity . . . . 32
3.4 Derivation of the semi-supervised learning algorithm . . . . 35
3.5 Experiments . . . . 38
3.6 Related Work . . . . 45
3.7 Conclusions . . . . 45
4 Learning the similarity on very large graphs 47 4.1 Introduction . . . . 47
4.2 Efficient computation of sum of similarities . . . . 48
4.3 Related work . . . . 49
4.4 Problem definition . . . . 51
4.5 Learning the similarity . . . . 52
4.6 Avoiding the assumption of homophily . . . . 55
4.7 Experiments . . . . 56 4
contents 5
4.8 Conclusion . . . . 62
II Collaborative Filtering 63 5 Introduction 65 5.1 Collaborative filtering . . . . 66
5.2 Families of collaborative filtering algorithms . . . . 68
5.3 Training matrix factorisation methods with stochastic gradient descent . . . . 73
5.4 Evaluating recommender systems . . . . 75
5.5 Contributions . . . . 76
6 Dynamic Matrix Factorisation with Priors on Unknown Values 79 6.1 Introduction . . . . 79
6.2 Standard matrix factorisation . . . . 81
6.3 Interpreting missing data . . . . 81
6.4 Objective functions . . . . 82
6.5 Experiments . . . . 88
6.6 Related Work . . . . 96
6.7 Conclusions . . . . 98
7 Sequence-based Collaborative Filtering with Recurrent Neu- ral Networks 99 7.1 Introduction . . . . 99
7.2 Sequence-based collaborative filtering . . . . 100
7.3 Collaborative filtering with recurrent neural networks . . . . . 104
7.4 Methods comparison . . . . 111
7.5 Short-term / long-term profile . . . . 114
7.6 Other variations of the RNN . . . . 118
7.7 Conclusion . . . . 120
8 Accelerating model-based collaborative filtering with item clustering 121 8.1 Introduction . . . . 121
8.2 Related Works . . . . 122
8.3 Method . . . . 123
8.4 Experiments . . . . 128
8.5 Discussion . . . . 134
8.6 Conclusion . . . . 134
9 General conclusion 137 9.1 Findings and contributions . . . . 137
9.2 Improving recommender systems . . . . 140
9.3 Concerns and perspectives . . . . 141
A Collaborative filtering based on sequences 143 A.1 Installation . . . . 143
A.2 Usage . . . . 143
A.3 Methods . . . . 147
6 contents
Bibliography 153
