Dr. Nima Dokoohaki is a senior data scientist currently affiliated with Accenture Applied Intelligence, a leading data and analytics service provider world wide. In addition, he also maintains collaboration as an external advisor, with a research group at Software and Computer Systems department, a part of School for Electrical Engineering and Computer Science at Royal Institute of Technology (KTH). His research interests include Trust & Privacy, Applied Machine Learning, Social Networks and Recommendation Systems. He received his Ph.D. in information and communications technology (ICT) in 2013. The main theme of his research was how to understand and leverage the notion of computational and social trust so online service providers can deliver more transparent and privacy preserving analytical services to their end users. He explored application of his research onto social network data and recommendation systems. His research has been backed by European projects funded from EU FP7 and Horizon 2020 framework programs, as well as distinguished public funding organizations including Swedish Research Council and Vinnova. After graduation, he received a distinguished fellowship from the European Research Consortium for Informatics and Mathematics (ERCIM) where he worked on large scale data and analytics. He has published over 40 peer-reviewed articles. In addition to two best paper awards, he has been interviewed for his visible research and his lecture has been broadcasted on Swedish public television. He is an ACM professional member, he is a certified reviewer for prestigious Knowledge and Information Systems (KAIS) as well as occasional reviewer for recognized international venues and journals. 

Part 1. Cold Start in Recommendations.- Chapter 1. Fashion Recommender Systems in Cold Start ( Mehdi Elahi).- Part 2. Complementary and Session Based Recommendation.- Chapter 2. Enabling Hyper-Personalisation: Automated AdCreative Generation and Ranking for Fashion e-Commerce (Sreekanth Vempati).- Chapter 3. Two-Stage Session-based Recommendations with Candidate Rank Embeddings (Jose Antonio Sanchez Rodrguez).- Part 3. Outfit Recommendations.- Chapter 4. Attention-based Fusion for Outfit Recommendation (Katrien Laenen).- Chapter 5. Outfit2Vec: Incorporating Clothing Hierarchical MetaData into Outfits' Recommendation (Shatha Jaradat).- Part 4. Sizing and Fit Recommendations.- Chapter 6. Learning Size and Fit from Fashion Images (Nour Karessli).- Part 5. Generative Outfit Recommendation.- Chapter 7. Generating High-Resolution Fashion Model Images Wearing Custom Outfits (Gokhan Yildirim).