Fashion Recommender Systems 9783030552176, 9783030552183

Table of contents :
Acknowledgement
Contents
Part I Cold Start in Recommendations
Fashion Recommender Systems in Cold Start
1 Introduction
2 Techniques for Fashion Recommendation
3 Cold Start
4 Potential Solutions
4.1 Item Side Information Approaches
4.2 User Side Information Approaches
4.3 Approaches Based on Implicit Preferences
4.4 Cross-Domain Approaches
4.5 Rating Elicitation Approaches
5 Conclusion
References
Part II Complementary and Session Based Recommendation
Enabling Hyper-Personalisation: Automated Ad Creative Generation and Ranking for Fashion e-Commerce
1 Introduction
2 Related Work
3 Methodology
3.1 Automated Annotation of Photo-Shoot Images
3.1.1 Object and Person Detection
3.1.2 Fashion Category Detection
3.1.3 Gender and Face Detection
3.1.4 Scene Detection
3.1.5 Text Detection
3.2 Layout Generation
3.3 Creative Generation
3.3.1 Cropping and Scaling Input Image
3.3.2 Overlaying Text Callouts
3.4 Ranking Creatives
4 Experiments and Results
4.1 Qualitative Evaluation of Generated Layouts
4.2 Qualitative Evaluation of Cropping and Scaling Algorithm
4.2.1 Baseline Approach
4.3 Qualitative Evaluation of Generated Creatives
4.4 Evaluation of the Ranking Model
4.4.1 Evaluation Metrics
4.4.2 Quantitative Evaluation
4.4.3 Qualitative Evaluation
4.5 Evaluation of the Complete Approach
5 Applications
6 Conclusion
References
Two-Stage Session-Based Recommendations with Candidate Rank Embeddings
1 Introduction
2 Related Work
2.1 Session-Based Recommender Systems
2.2 Two-Stage Approaches
3 Problem Statement
4 Two-Stage Recommender with Candidate Rank Embeddings
4.1 The Candidate Generator
4.2 The Re-ranker
5 Experiments and Analysis
5.1 Datasets
5.2 Evaluation Metrics
5.3 Baselines
5.4 Experimental Setup
5.5 Predicting Fashion-Similar Target Clicks
5.6 Predicting the Next Click
5.7 Offline Results and Analysis
5.8 Online Experiment at Zalando
6 Conclusion and Future Work
References
Part III Outfit Recommendations
Attention-Based Fusion for Outfit Recommendation
1 Introduction
2 Related Work
3 Methodology
3.1 Common Space Fusion
3.2 Attention-Based Fusion
3.2.1 Visual Dot Product Attention
3.2.2 Stacked Visual Attention
3.2.3 Visual L-Scaled Dot Product Attention
3.2.4 Co-attention
4 Experimental Setup
4.1 Experiments and Evaluation
4.2 Baselines
4.3 Datasets
4.3.1 Polyvore68K
4.3.2 Polyvore21K
4.4 Comparison with Other Works
4.5 Training Details
5 Results
6 Conclusion
Appendix
A Dataset Item Types
References
Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits' Recommendation
1 Introduction
2 Related Works
3 Methodology
3.1 Methodology for Generating Outfits' Representative Vectors
3.1.1 Mapping of Item Details into Clothing Entities
3.1.2 Projecting Clothing Entities into Outfit Vectors
3.2 Outfit2Vec and PartialOutfit2Vec Models
4 Experimental Pipeline
4.1 Datasets
4.2 Whole Outfits Recommendation (Outfit2Vec)
4.3 Partial Outfits Recommendation
4.4 MultiClass Classification Evaluation
4.5 Discussion
5 Conclusions and Future Work
References
Part IV Sizing and Fit Recommendations
Learning Size and Fit from Fashion Images
1 Introduction
2 Related Work
3 Proposed Approach
3.1 Teacher-Student Learning
3.2 Statistical Modeling
3.3 SizeNet: Learning Visual Size and Fit Cues
3.3.1 Backbone Feature Extractor
3.3.2 Multi-layer Perceptron
4 Experimental Results and Discussion
4.1 Dataset
4.2 Evaluation
4.2.1 Baselines
4.2.2 Weight Importance
4.3 Brand Size Issue Scoring
4.4 Visualization of Size Issue Cues
5 Conclusion
References
Part V Generative Outfit Recommendation
Generating High-Resolution Fashion Model Images Wearing Custom Outfits
1 Introduction
2 Outfit Dataset
3 Methods
3.1 Unconditional
3.2 Conditional
4 Experiments
4.1 Unconditional
4.2 Conditional
4.3 Quantitative Results
5 Conclusion
References