I’m approaching the final stretch of my thesis on how language usage affects classical versus modern re-rankers in multilingual e-commerce search, and I need an experienced collaborator to help me finish strong within the next two weeks. Current status • Topic, research questions, and overall structure are approved. • Roughly 20 % of the introduction, background, and related-work sections are drafted. • Key literature has been gathered and annotated. What I still need • Expansion and polishing of the theoretical chapters (background and related work) so they meet APA guidelines and read cohesively. • Design, implementation, and clear documentation of experiments that compare traditional re-ranking methods with Transformer-based or LLM-driven models on a multilingual e-commerce dataset. • Guidance on selecting the best dataset (publicly available e-commerce datasets, such as the Amazon Product Review dataset or Yoochoose), plus full preprocessing and cleaning. • Drafting of the results, discussion, and conclusion chapters, tying findings back to the research questions. The technical stack is expected to revolve around Python, PyTorch or TensorFlow, Hugging Face Transformers, and standard evaluation frameworks (e.g., scikit-learn metrics, IR evaluation packages). Proficiency with recommendation or search ranking algorithms is essential. Deliverables (all in APA style) 1. Completed background and related-work chapters, fully referenced. 2. Reproducible experiment code, notebooks, and README. 3. Cleaned and documented dataset ready for submission. 4. Results section with tables/figures, followed by a critical discussion and concise conclusion. 5. A short hand-over note outlining next steps or possible future work. Acceptance criteria • Writing is free of grammatical errors and follows APA7 strictly. • Experiments run end-to-end on my machine without modification. • Results and discussion directly answer the research questions and highlight practical implications for multilingual e-commerce search. !!! All written content must be fully original, natural, and human-like in style. Texts should pass common AI detection tools (such as GPTZero or Turnitin AI detection) and meet academic writing standards. No direct AI-generated or machine paraphrased content is accepted. If you have a solid academic writing portfolio in machine learning/NLP and hands-on experience with Transformer architectures and ranking algorithms, let’s connect and wrap this thesis up on schedule. !!!