activity
20172021
most citedFantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

12 citations · 33 across the 10 of their papers we have counts for

collaborators

10 papers

cs.IR2021★ 3 cited

"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops

Patrick John Chia, Bingqing Yu, Jacopo Tagliabue

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…

cs.IR2021★ 1 cited

Query2Prod2Vec Grounded Word Embeddings for eCommerce

Federico Bianchi, Jacopo Tagliabue, Bingqing Yu

We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent spac…

cs.CL2020★ 2 cited

BERT Goes Shopping: Comparing Distributional Models for Product Representations

Federico Bianchi, Bingqing Yu, Jacopo Tagliabue

Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}. Inspired by the recent performance improvements on several NLP task…

cs.LG2020★ 3 cited

Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning

Bingqing Yu, Jacopo Tagliabue

We tackle tag-based query refinement as a mobile-friendly alternative to standard facet search. We approach the inference challenge with reinforcement learning, and propose a deep…

cs.IR2020★ 12 cited

Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

Federico Bianchi, Jacopo Tagliabue, Bingqing Yu +2

This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping int…

cs.AI2020★ 4 cited

Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution

Jacopo Tagliabue, Bingqing Yu

We tackle the challenge of in-session attribution for on-site search engines in eCommerce. We phrase the problem as a causal counterfactual inference, and contrast the approach wit…

cs.LG2020

How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead

Jacopo Tagliabue, Bingqing Yu, Marie Beaulieu

In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahea…

cs.IR2020★ 3 cited

"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization

Bingqing Yu, Jacopo Tagliabue, Ciro Greco +1

We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare. We focus on in-sess…

cs.CV2017★ 5 cited

Personalization of Saliency Estimation

Bingqing Yu, James J. Clark

Most existing saliency models use low-level features or task descriptions when generating attention predictions. However, the link between observer characteristics and gaze pattern…

cs.CV2017

WAYLA - Generating Images from Eye Movements

Bingqing Yu, James J. Clark

We present a method for reconstructing images viewed by observers based only on their eye movements. By exploring the relationships between gaze patterns and image stimuli, the "Wh…