output
20202025
most citedA Comprehensive Review of Modern Object Segmentation Approaches

43 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing cs.IRShow all

6 papers · 1 filter

cs.IR2025★ 2 cited

Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap

Srivatsa Mallapragada, Ying Xie, Varsha Rani Chawan +2

E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that l…

cs.IR2022★ 22 cited

M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

Walid Shalaby, Sejoon Oh, Amir Afsharinejad +2

Session-based recommender systems (SBRSs) have shown superior performance over conventional methods. However, they show limited scalability on large-scale industrial datasets since…

cs.IR2021★ 9 cited

Diversifying Multi-aspect Search Results Using Simpson's Diversity Index

Jianghong Zhou, Eugene Agichtein, Surya Kallumadi

In search and recommendation, diversifying the multi-aspect search results could help with reducing redundancy, and promoting results that might not be shown otherwise. Many previo…

cs.IR2021★ 7 cited

De-Biased Modelling of Search Click Behavior with Reinforcement Learning

Jianghong Zhou, Sayyed M. Zahiri, Simon Hughes +3

Users' clicks on Web search results are one of the key signals for evaluating and improving web search quality and have been widely used as part of current state-of-the-art Learnin…

cs.IR2021

APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization

Ali Ahmadvand, Sayyed M. Zahiri, Simon Hughes +3

Query categorization is an essential part of query intent understanding in e-commerce search. A common query categorization task is to select the relevant fine-grained product cate…

cs.IR2020★ 1 cited

JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

Ali Ahmadvand, Surya Kallumadi, Faizan Javed +1

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in…