6 papers
Extracting Important Tokens in E-Commerce Queries with a Tag Interaction-Aware Transformer Model
Md. Ahsanul Kabir, Mohammad Al Hasan, Aritra Mandal +4
The major task of any e-commerce search engine is to retrieve the most relevant inventory items, which best match the user intent reflected in a query. This task is non-trivial due…
Hierarchical Job Classification with Similarity Graph Integration
Md Ahsanul Kabir, Kareem Abdelfatah, Mohammed Korayem +1
In the dynamic realm of online recruitment, accurate job classification is paramount for optimizing job recommendation systems, search rankings, and labor market analyses. As job m…
Extracting Cause-Effect Pairs from a Sentence with a Dependency-Aware Transformer Model
Md Ahsanul Kabir, Abrar Jahin, Mohammad Al Hasan
Extracting cause and effect phrases from a sentence is an important NLP task, with numerous applications in various domains, including legal, medical, education, and scientific res…
Retrieval Augmented Generation based Large Language Models for Causality Mining
Thushara Manjari Naduvilakandy, Hyeju Jang, Mohammad Al Hasan
Causality detection and mining are important tasks in information retrieval due to their enormous use in information extraction, and knowledge graph construction. To solve these ta…
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…
Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs
Md Ahsanul Kabir, Kareem Abdelfatah, Shushan He +2
As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optim…