"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops
arXiv:2107.03256
Abstract
Large eCommerce players introduced comparison tables as a new type of recommendations. However, building comparisons at scale without pre-existing training/taxonomy data remains an open challenge, especially within the operational constraints of shops in the long tail. We present preliminary results from building a comparison pipeline designed to scale in a multi-shop scenario: we describe our design choices and run extensive benchmarks on multiple shops to stress-test it. Finally, we run a small user study on property selection and conclude by discussing potential improvements and highlighting the questions that remain to be addressed.
Accepted for publication at SIGIR eCom 2021
References in corpus (5)
- Inferring Networks of Substitutable and Complementary Products
- AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
- Recent Advances in Diversified Recommendation
- Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
- Learning Item-Interaction Embeddings for User Recommendations