#5: Fashion Recommendations with Zeno Gantner
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.
Links from this Episode:
- Preferably reach out to Zeno Gantner via email (find his address mentioned by the end of the episode)
Fashion DNA by Zalando Research (Paper)
Fashion MNIST (image dataset)
- Workshop on Recommender Systems in Fashion 2021
- RecSys Challenge 2022 on Session-based Fashion Item Recommendation by Dressipi
- H&M Personalized Fashion Recommendation Challenge on Kaggle
- Spotify: A Product Story - Episode 4: Human vs Machine
- Dataset for trivago RecSys Challenge 2019
- RecSys 2020: Tutorial on Conversational Recommender Systems
- Rendle et al. (2009): Bayesian Personalized Ranking from Implicit Feedback (2009)
- Loni et al. (2016): Bayesian Personalized Ranking with Multi-Channel User Feedback
- Sheikh et al. (2019): A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce
- Wilhelm et al. (2018): Practical Diversified Recommendations on YouTube with Determinantal Point Processes
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