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.

翻译：孟加拉国电商平台拥有数百万条用孟加拉语Unicode、英语和Banglish（孟加拉语用拉丁字母音译）撰写产品评论。然而，代码混合评论对推荐性能的影响在很大程度上仍未得到探讨。我们首次对Daraz孟加拉国的产品评论进行基准测试，在按用户时间顺序留一法的协议下评估了六个模型族。为应对数据集严重的长期稀疏性问题（59.3%的用户仅有单次交互），我们在五种密度配置下进行了系统的k-core阈值消融实验。结果显示：基于物品的协同过滤在所有设置中保持稳定，隐式矩阵分解随密度降低而性能急剧下降，而显式矩阵分解在高阈值下表现独特提升。为表征代码混合对推荐质量的影响，我们使用字符n-gram TF-IDF配置文件对基于内容过滤进行语言分层评估。结果提供实证证据：相较于孟加拉语文字用户，Banglish词汇碎片化导致NDCG@10降低46.8%，这种性能退化可追溯到不同表面形式间的音译不一致性。本研究为代码混合、低资源电商环境下的推荐研究建立了可复现的评估基础。代码已公开发布于：https://github.com/os-car-war-thy/daraz-recsys