# Leveraging Code-Mixed Product Metadata and User Feedback for Personalized Recommendation on Daraz Bangladesh

arXiv: https://arxiv.org/abs/2606.16387 · HTML: https://mlsanity.com/paper/2606.16387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2026.

## Built on by (most cited first, 1 of 1)

- [A Benchmark for Bangla Task-Oriented Dialogue Understanding](https://mlsanity.com/paper/2610.18060/) (2026)

## Abstract (arXiv)

Bangladeshi e-commerce platforms host millions of product reviews written in Bengali Unicode, English, and Banglish, where Bengali is phonetically transcribed in Latin script. However, the impact of code-mixed reviews on recommendation performance remains largely unexplored. We present the first such benchmarking on product reviews from Daraz Bangladesh, evaluating six model families under a per-user chronological leave-last-out protocol. To address the severe long-tail sparsity of the dataset, where 59.3% of users have exactly one interaction, we conduct a systematic k-core threshold ablation across five density configurations. The results reveal that Item-based Collaborative Filtering remains stable across settings, Implicit Matrix Factorization degrades sharply with decreasing density, and Explicit Matrix Factorization uniquely improves at higher thresholds. To characterize the impact of code-mixing on recommendation quality, we perform a language-stratified evaluation of content-based filtering using character n-gram TF-IDF profiles. The results provide empirical evidence that fragmentation of the Banglish vocabulary reduces NDCG@10 by 46.8% relative to Bengali-script users, a degradation traceable to transliteration inconsistency across surface forms. This work establishes a reproducible evaluation foundation for recommendation research in code-mixed, low-resource e-commerce settings. The code is publicly available at https://github.com/os-car-war-thy/daraz-recsys.