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Intelligent, Item Based Stereotype Recommender System

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Intelligent, Item-Based Stereotype Recommender System NOURAH ABDULMOHSEN AL-ROSSAIS Ph.D. Thesis Computer Science Feb 2021 To my beloved father, my role model and inspiration who has sadly passed away during my study ... You will always motivate me to achieve more in life ... Abstract Recommender systems (RS) have become key components driving the success of e-commerce, and other platforms where revenue and customer satisfaction is dependent on the user’s ability to discover desirable items in large catalogues. As the number of users and items on a platform grows, the computational complexity, the vastness of the data, and the sparsity problem constitute important challenges for any recommendation algorithm. In addition, the most widely studied filtering-based RS, while effective in providing suggestions for established users and items, are known for their poor performance for the new user and new item (cold start) problems. Stereotypical modelling of users and items is a promising approach to solving these problems. A stereotype represents an aggregation of the characteristics of the items or users which can be used to create general user or item classes. This work propose a set of methodologies for the automatic generation of stereotypes during the cold-starts. The novelty of the proposed approach rests on the findings that stereotypes built independently of the user-to-item ratings improve both recommendation metrics and computational performance during cold-start phases. The resulting RS can be used with any machine learning algorithm as a solver, and the improved performance gains due to rate-agnostic stereotypes are orthogonal to the gains obtained using more sophisticated solvers. Recommender Systems using the primitive metadata features (baseline systems) as well as factorisation-based systems are used as benchmarks for state-of-the-art methodologies to assess the results of the proposed approach under a wide range of recommendation quality metrics. The results demonstrate how such generic groupings of the metadata features, when performed in a manner that is unaware and independent of the user’s community preferences, may greatly reduce the dimension of the recommendation model, and provide a framework that improves the quality of recommendations in the cold start. 3 Contents List of Tables . 10 List of Figures . 14 Acknowledgements . 15 Declaration . 16 Publications . 17 1 Introduction 18 1.1 Background and Rationals . 18 1.2 Problem Statement . 20 1.3 Research Questions and Hypothesis . 22 1.4 Structure of the Thesis . 22 2 Literature Review 24 2.1 Recommender System in e-commerce . 24 2.2 Popular Recommender Systems . 26 2.3 Existing Recommender System Approaches . 27 2.3.1 Collaborative Filtering (CF) . 27 2.3.2 Content-Based Filtering (CBF) . 29 2.3.3 Demographic . 30 2.3.4 Hybrid . 30 2.3.5 Pairwise Preference Learning . 31 2.4 Cold-Start Problem for Recommender Systems . 31 2.5 Stereotype-Based Modeling . 33 2.5.1 Stereotype - Definition and Evolution . 33 2.5.2 Stereotypes in Recommender Systems . 37 2.5.3 Approaches for Stereotypical Groups . 39 2.6 Evaluation of Recommender Systems . 41 2.7 Summary . 42 3 Problem Analysis 44 3.1 Research Methodology . 45 3.2 MovieLens 1 Million Dataset . 46 3.3 Clustering Algorithms . 48 4 CONTENTS 5 3.4 Experimental Evaluation . 49 3.5 Summary . 68 4 Automatic Construction of Item-Based Stereotypes 70 4.1 Stereotypes for Complex Categorical Features . 70 4.2 Stereotypes for Numerical Features . 74 4.3 Stereotype Creation Experiment . 80 4.3.1 Results with Complex Categorical Features . 80 4.3.2 Results with Numerical Features . 96 4.4 Summary . 102 5 Preliminary Evaluation of Stereotypes 103 5.1 Homogeneity of Training and Test Datasets . 104 5.2 Stereotypes Evaluation . 106 5.2.1 A Hard Test . 106 5.2.2 A Soft Test . 114 5.2.3 Predictive Power of Stereotypes . 122 5.3 Summary . 124 6 Stereotype-Based Recommendation Performance 125 6.1 Experimental Evaluation . 126 6.1.1 Cold Start Assessment of Item Consumption . 127 6.1.2 Cold Start Assessment of Item Rating . 133 6.1.3 Cold Start Assessment of Recommendations Driven by Stereotypes versus SVD-Based RS (with metadata) . 139 6.2 Summary . 149 7 Validation of the Stereotype-Driven Methodology 151 7.1 Amazon Dataset . 152 7.2 Constructing Item-Based Stereotypes . 153 7.2.1 Stereotypes for Complex Categorical Features . 154 7.2.2 Stereotypes for the Numerical Features . 159 7.3 Evaluation of Stereotypes . 163 7.3.1 Homogeneity of Training and Test Datasets . 163 7.3.2 A Hard Test . 166 7.3.3 A Soft Test . 171 7.3.4 Predictive Power of Stereotypes . 174 7.4 Recommendation Performance . 176 7.4.1 Cold Start Assessment of Item Rating . 177 7.4.2 Cold Start Assessment of Recommendations Driven by Stereotypes versus SVD-Based RS (with metadata) . 184 7.5 Summary . 191 8 Conclusion and Future Work 193 8.1 Conclusion . 193 8.2 Contribution to knowledge . 196 8.3 Limitations and Future Work . 199 References 201 List of Tables 3.1 Combined ML 1 Million/IMDb movie and user features ................. 47 3.2 Movie features transformed to a numerical feature vector for Analysis 1 and Analysis 1.B 49 3.3 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to k-means ................................... 59 3.4 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to GMM .................................... 59 3.5 Top 7 ranked feature directions (or subspace of directions) according to maximum separ- n;a ation as measured by δj for DBSCAN ........................ 59 3.6 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to k-means for Analysis 1.B ........................... 64 3.7 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to gaussian mixture model (GMM) for Analysis 1.B ............... 64 3.8 Top 7 ranked feature directions (or subspace of directions) according to maximum separ- n;a ation as measured by δj for DBSCAN for Analysis 1B ................ 64 3.9 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to k-means for Analysis 1.C .......................... 67 3.10 Top 7 ranked feature directions (or subspace of directions) according to maximum sep- n;a aration as measured by δj for an increasing number of clusters (n) and algorithm (a) equals to GMM for Analysis 1.C ........................... 68 3.11 Top 7 ranked feature directions (or subspace of directions) according to maximum separ- n;a ation as measured by δj for DBSCAN for Analysis 1.C ................ 68 4.1 Stereotypes automatically generated using algorithm 1 for the feature: genre and keywords 92 4.2 k-modes resulting centroids composition for 5 clusters and the genre feature, with two alternative methodologies for the initialisation of the position of the centroids ...... 94 4.3 k-modes resulting centroids composition for 10 clusters and the genre feature, with two alternative methodologies for the initialisation of the position of the centroids ...... 94 6 LIST OF TABLES 7 4.4 Centroid composition identified by k-modes for the feature keywords with Huang initial- isation methodology and k= 20 ............................ 95 4.5 Filtered modes discovered and ranked via the persistence algorithm for feature: log(budget+1) 97 4.6 Filtered modes discovered and ranked via the persistence algorithm for feature: log(revenue+1) 97 4.7 Filtered modes discovered and ranked via the persistence algorithm for feature: director popularity ....................................... 97 4.8 Filter modes discovered and ranked via the persistence algorithm for feature: country distance ........................................ 97 4.9 Filtered modes discovered and ranked via the persistence algorithm for feature: cast pop- ularity ........................................ 97 4.10 Filter modes discovered and ranked via the persistence algorithm for feature: language popularity ....................................... 97 4.11 Filter modes discovered and ranked via the persistence algorithm for feature: cast gender bias .......................................... 98 4.12 Filtered modes discovered and ranked via the persistence algorithm for feature: popularity of movie ....................................... 98 4.13 Filtered modes discovered and ranked via the persistence algorithm for feature: production company popularity .................................. 98 4.14 Filtered modes discovered and ranked via the persistence algorithm for feature: release time of the year .................................... 98 4.15 Filtered modes discovered and ranked via the persistence algorithm for feature: vote average 98 4.16 Filtered modes discovered and ranked via the persistence algorithm for feature: release year 98 4.17 Filtered modes discovered and ranked via the persistence algorithm for feature: runtime . 99 4.18 Filtered modes discovered and ranked via the persistence algorithm for feature: log(vote count) ........................................ 99 4.19 Classification
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