Search Results for author: Choon Hui Teo

Found 7 papers, 2 papers with code

MICO: Selective Search with Mutual Information Co-training

1 code implementation COLING 2022 Zhanyu Wang, Xiao Zhang, Hyokun Yun, Choon Hui Teo, Trishul Chilimbi

In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.


On the Value of Behavioral Representations for Dense Retrieval

no code implementations11 Aug 2022 Nan Jiang, Dhivya Eswaran, Choon Hui Teo, Yexiang Xue, Yesh Dattatreya, Sujay Sanghavi, Vishy Vishwanathan

We consider text retrieval within dense representational space in real-world settings such as e-commerce search where (a) document popularity and (b) diversity of queries associated with a document have a skewed distribution.

Retrieval Text Retrieval

Embracing Structure in Data for Billion-Scale Semantic Product Search

no code implementations12 Oct 2021 Vihan Lakshman, Choon Hui Teo, Xiaowen Chu, Priyanka Nigam, Abhinandan Patni, Pooja Maknikar, SVN Vishwanathan

When training a dyadic model, one seeks to embed two different types of entities (e. g., queries and documents or users and movies) in a common vector space such that pairs with high relevance are positioned nearby.

A Study of Context Dependencies in Multi-page Product Search

no code implementations9 Sep 2019 Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft

In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.


Leverage Implicit Feedback for Context-aware Product Search

no code implementations4 Sep 2019 Keping Bi, Choon Hui Teo, Yesh Dattatreya, Vijai Mohan, W. Bruce Croft

However, customers with little or no purchase history do not benefit from personalized product search.


Semantic Product Search

1 code implementation1 Jul 2019 Priyanka Nigam, Yiwei Song, Vijai Mohan, Vihan Lakshman, Weitian, Ding, Ankit Shingavi, Choon Hui Teo, Hao Gu, Bing Yin

To address these issues, we train a deep learning model for semantic matching using customer behavior data.

Adaptive, Personalized Diversity for Visual Discovery

no code implementations2 Oct 2018 Choon Hui Teo, Houssam Nassif, Daniel Hill, Sriram Srinavasan, Mitchell Goodman, Vijai Mohan, SVN Vishwanathan

Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired.

Cannot find the paper you are looking for? You can Submit a new open access paper.