Decomposing fit semantics for product size recommendation in metric spaces
By Rishabh Misra, Mengting Wan, Julian McAuley. 2018
59 citations recorded by the source corpus.
Product size recommendation and fit prediction are critical in order to improve customers' shopping experiences and to reduce product return rates. Modeling customers' fit feedback is challenging due to its subtle semantics, arising from the subjective evaluation of products, and imbalanced label distribution. In this paper, we propose a new predictive framework to tackle the product fit problem, which captures the semantics behind customers' fit feedback, and employs a metric learning technique to resolve label imbalance issues. We also contribute two public datasets collected from online clothing retailers.
Subfields: Artificial Intelligence, Marketing, Recommender Systems
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In Artificial Intelligence
- Ruiqi Zhong
- Zheng Zhang
- Daniel Klein
- Luke S. Zettlemoyer
- Kristy Lee
- Hannaneh Hajishirzi
- Jianfeng Gao
- Percy Liang
Citations in the corpus
- Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections (2021, background): “Clothing Fitness (Misra et al., 2018) Checking whether the customer complains that the cloth is too small or too large.”
- Meta-tuning Language Models to Answer Prompts Better (2021, background): “Clothing Fitness (Misra et al., 2018) Checking whether the customer complains that the cloth is too small or too large.”