#5: Fashion Recommendations with Zeno Gantner

Episode number five of Recsperts revolves around fashion recommendations in general and at Zalando in specific. My guest is Zeno Gantner, who is a principal applied scientist and works in one of several personalization teams at Zalando. As an individual contributor and part of the leadership team he drives personalization not only to recommend relevant clothing, but also to facilitate inspiration and discovery for Zalando’s customers. With a background in computer science and symbolic AI, Zeno spent his PhD on ML applied to recommender systems and contributed to various open source projects as well as served at RecSys as member of the senior program committee.

In episode five my guest is Zeno Gantner, who is a principal applied scientist at Zalando. Zeno obtained his PhD from the University of Hildesheim where he was investigating ML-based recommender systems. As a principal applied scientist he is responsible for strategy, mentoring and setting standards for different initiatives on fashion recommendations impacting over 48 million customers in Europe.

We discuss the ramifications and limitations of positive-only implicit feedback, touch on how reinforcement learning and more rating-like feedback can help as well as how to treat multiple feedback levels. In the main part, we turn our focus towards fashion recommendations and the “usual suspects” of typical e-commerce recommender systems.  We also discuss the goal of creating more fashion-specific recommendations and making users come back for inspiration. This involves a lot of domain-specific modeling and design of experiences to cater the needs for various user segments: from fashionistas to pragmatic customers. This also involves putting users into the “driver seat” of recommenders as well as understanding how to achieve long-term customer satisfaction.

Finally, we briefly touch on the topic of size and fit recommendations and finish with an outlook on the future developments leading to fashion recommendations becoming its own subfield within the recommender systems space.

Enjoy this enriching episode of RECSPERTS - Recommender Systems Experts.

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