Recommender Systems in Practice: A Business Value Driven Approach for Startups

Recommender systems are a type of solution to overcome the information overload problem our society is experiencing. The value of recommender systems is proven by many of the leading online businesses such as Amazon, eBay and Spotify. The field of recommender systems is vastly studied resulting in thousands of di↵erent publications, articles and theories. Ironically, this amount is a form of information overload by itself. Organizations face significant challenges in designing a recommender system that truly delivers business value. The main finding of this thesis is that specifically startups are a↵ected by this because of their unique circumstances: extreme uncertainty, low time-to-market and unknown customer demands. Inspired by the leading online businesses, some startups have the idea to o↵er personalized recommendations too. However, when diving into the field of recommender systems, startups might get overwhelmed, do not know where to get started and face certain challenges. This thesis outlines distinct capabilities of recommender systems and links these with challenges startups face. Ultimately, the goal of this research is to help startups in the process of designing, operating and maintaining a recommender system. To achieve this goal, a framework is proposed that consists of 7 steps: from business analysis to the development and monitoring of a recommender system. Tools are provided to help startups make the right decisions in designing a recommender system that truly delivers business value. To assess the complexity, required e↵ort and risk involved in certain recommendation ideas, a dimension model was developed. This model consists of eight dimensions: user model, item model, domain model, performance, quality of outcomes, architecture and infrastructure, interface & user experience and privacy & security. Master’s Thesis S.B. Mulders Version: 1.2 (September 18, 2017) Page 1 of 155 Acknowledgements First and foremost, I would like to express my gratitude for my supervisor, Tino de Rijk. Your passionate commitment, helpful discussions, and insightful guidance have been very useful. I am also thankful for my second supervisor, Prof. dr. ir. Wessel Kraaij. Your dedication in a busy period and your valuable comments are much obliged. Furthermore, Tim de Kraker deserves a special mention. Your input and insights during the entire process have greatly benefitted this study. A word of gratitude is also reserved for my close friend and business partner Diederik van den Burger. Our brainstorm sessions and your proofreading have helped me a great deal. Another word of thanks to Menno Schellekens for his time for testing the research outcomes in the form of a case study. I would also like to thank Niels Basjes for helping me validate the outcomes of this research and Gino Bazzoni for motivating me throughout the process and bringing me into contact with Niels Basjes. Naturally, I want to gratefully thank my parents, family, and friends for believing in me and providing me with unfailing support and encouragement. Finally, special thanks goes out to my good friends Kaylesh Ajodhia, Hugo Brouwer and Dylan te Lindert. We were in the same boat together, and we managed to keep each other motivated when times got hard. Master’s Thesis S.B. Mulders Version: 1.2 (September 18, 2017) Page 2 of 155