A Hierarchical Bayesian Model for Size Recommendation in Fashion
| Autor: | Romain Guigourès, Reza Shirvany, Evgenii Koriagin, Yuen King Ho, Abdul-Saboor Sheikh, Urs Bergmann |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Hierarchy (mathematics) Computer science Bayesian probability Machine Learning (stat.ML) 02 engineering and technology Recommender system Bayesian inference computer.software_genre 01 natural sciences Machine Learning (cs.LG) 010104 statistics & probability Joint probability distribution Statistics - Machine Learning Prior probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Multinomial distribution Data mining 0101 mathematics computer Event (probability theory) |
| Zdroj: | RecSys |
| DOI: | 10.48550/arxiv.1908.00825 |
| Popis: | We introduce a hierarchical Bayesian approach to tackle the challenging problem of size recommendation in e-commerce fashion. Our approach jointly models a size purchased by a customer, and its possible return event: 1. no return, 2. returned too small 3. returned too big. Those events are drawn following a multinomial distribution parameterized on the joint probability of each event, built following a hierarchy combining priors. Such a model allows us to incorporate extended domain expertise and article characteristics as prior knowledge, which in turn makes it possible for the underlying parameters to emerge thanks to sufficient data. Experiments are presented on real (anonymized) data from millions of customers along with a detailed discussion on the efficiency of such an approach within a large scale production system. |
| Databáze: | OpenAIRE |
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