Recommender Systems support the decision making processes of customers with personalized suggestions. They are widely used and influence the daily life of almost everyone in different domains like e-commerce, social media, or entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users’ varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge.
Meanwhile, the pervasive application of deep learning proves to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. In my work I provide a deep learning approach towards personalized recommendations of vehicles at mobile.de. I propose a novel regularization technique for the optimization criterion and evaluate it against various baselines. To achieve high scalability I combine this method with strategies for efficient candidate generation based on user and item embeddings. Thus, we provide a holistic solution for candidate generation and ranking.
The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds.
Marcel Kurovski is a Data Scientist at inovex, a German IT project house focusing on digital transformation. He earned a master’s degree in Industrial Engineering and Management from the Karlsruhe Institute of Technology (KIT) where he focused on computer science, machine learning and operations research.
Marcel works on novel methods to exploit deep learning for recommender systems in order to better personalize content and improve user experience. He works for clients in e-commerce and retail where he bridges the gap between proof-of-concept and scalable AI systems. His research spans recommender systems, deep learning as well as methods for approximate nearest neighbor search.
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