1 citations · 1 across the 7 of their papers we have counts for
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LESER: Learning to Expand via Search Engine-feedback Reinforcement in e-Commerce
Yipeng Zhang, Bowen Liu, Xiaoshuang Zhang +3
User queries in e-commerce search are often vague, short, and underspecified, making it difficult for retrieval systems to match them accurately against structured product catalogs…
Bridging Modality Gaps in e-Commerce Products via Vision-Language Alignment
Yipeng Zhang, Hongju Yu, Aritra Mandal +3
Item information, such as titles and attributes, is essential for effective user engagement in e-commerce. However, manual or semi-manual entry of structured item specifics often p…
AI Guided Accelerator For Search Experience
Jayanth Yetukuri, Mehran Elyasi, Samarth Agrawal +4
Effective query reformulation is pivotal in narrowing the gap between a user's exploratory search behavior and the identification of relevant products in e-commerce environments. W…
A Survey on E-Commerce Learning to Rank
Md. Ahsanul Kabir, Mohammad Al Hasan, Aritra Mandal +2
In e-commerce, ranking the search results based on users' preference is the most important task. Commercial e-commerce platforms, such as, Amazon, Alibaba, eBay, Walmart, etc. perf…
Semantic Equivalence of e-Commerce Queries
Aritra Mandal, Daniel Tunkelang, Zhe Wu
Search query variation poses a challenge in e-commerce search, as equivalent search intents can be expressed through different queries with surface-level differences. This paper in…
Personalized Ranking in eCommerce Search
Grigor Aslanyan, Aritra Mandal, Prathyusha Senthil Kumar +2
We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a gen…