Search Results for author: Surya Kallumadi

Found 8 papers, 0 papers with code

De-Biased Modelling of Search Click Behavior with Reinforcement Learning

no code implementations21 May 2021 Jianghong Zhou, Sayyed M. Zahiri, Simon Hughes, Khalifeh Al Jadda, Surya Kallumadi, Eugene Agichtein

Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.

Learning-To-Rank reinforcement-learning

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

no code implementations21 May 2021 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.

DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search

no code implementations23 Apr 2021 Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein

Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e. g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

no code implementations18 Aug 2020 Jason Ingyu Choi, Surya Kallumadi, Bhaskar Mitra, Eugene Agichtein, Faizan Javed

Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.

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

no code implementations28 May 2020 Ali Ahmadvand, Surya Kallumadi, Faizan Javed, Eugene Agichtein

In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.

Active Learning

Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

no code implementations27 Dec 2019 Jon Degenhardt, Surya Kallumadi, Utkarsh Porwal, Andrew Trotman

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France.

Information Retrieval

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