Leveraging Translation For Optimal Recall: Tailoring LLM Personalization
  With User Profiles
Karthik Ravichandran Sarmistha Sarna Gomasta
This paper explores a novel technique for improving recall in cross-language
information retrieval (CLIR) systems using iterative query refinement grounded
in the user's lexical-semantic space. The proposed methodology combines
multi-level translation, semantic embedding-based expansion, and user
profile-centered augmentation to address the challenge of matching variance
between user queries and relevant documents. Through an initial BM25 retrieval,
translation into intermediate languages, embedding lookup of similar terms, and
iterative re-ranking, the technique aims to expand the scope of potentially
relevant results personalized to the individual user. Comparative experiments
on news and Twitter datasets demonstrate superior performance over baseline
BM25 ranking for the proposed approach across ROUGE metrics. The translation
methodology also showed maintained semantic accuracy through the multi-step
process. This personalized CLIR framework paves the path for improved
context-aware retrieval attentive to the nuances of user language.