8 papers
HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems
Pritom Saha Akash, Phanideep Gampa, Chao Shen +2
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Languag…
On Recommending Category: A Cascading Approach
Qihao Wang, Pritom Saha Akash, Varvara Kollia +3
Recommendation plays a key role in e-commerce, enhancing user experience and boosting commercial success. Existing works mainly focus on recommending a set of items, but online e-c…
Query Optimization for Parametric Knowledge Refinement in Retrieval-Augmented Large Language Models
Youan Cong, Pritom Saha Akash, Cheng Wang +1
We introduce the \textit{Extract-Refine-Retrieve-Read} (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RA…
RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems
Duy A. Nguyen, Rishi Kesav Mohan, Van Yang +2
Query rewriting (QR) is a critical technique in e-commerce search, addressing the lexical gap between user queries and product descriptions to enhance search performance. Existing…
Understanding Cross-Domain Adaptation in Low-Resource Topic Modeling
Pritom Saha Akash, Kevin Chen-Chuan Chang
Topic modeling plays a vital role in uncovering hidden semantic structures within text corpora, but existing models struggle in low-resource settings where limited target-domain da…
ERU-KG: Efficient Reference-aligned Unsupervised Keyphrase Generation
Lam Thanh Do, Aaditya Bodke, Pritom Saha Akash +1
Unsupervised keyphrase prediction has gained growing interest in recent years. However, existing methods typically rely on heuristically defined importance scores, which may lead t…