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

• URL: http://arxiv.org/abs/2606.16387v1
• Date: Mon, 15 Jun 2026 08:17:36 GMT
• Title: Leveraging Code-Mixed Product Metadata and User Feedback for Personalized Recommendation on Daraz Bangladesh
• Abstract summary: Bangladeshi e-commerce platforms host millions of product reviews written in Bengali Unicode, English, and Banglish, where Bengali is phonetically transcribed in Latin script.<n>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.
• Score: 1.1166163065402113
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification [0.10874100424278171]
  We introduce MixSarc, the first publicly available Bangla-English code-mixed corpus for implicit meaning identification.<n>The dataset contains 9,087 manually annotated sentences labeled for humor, sarcasm, offensiveness, and vulgarity.<n>Results show strong performance on humor detection but substantial degradation on sarcasm, offense, and vulgarity due to class imbalance and pragmatic complexity.
  arXiv  Detail & Related papers  (2026-02-25T06:12:06Z)
• BanglishRev: A Large-Scale Bangla-English and Code-mixed Dataset of Product Reviews in E-Commerce [2.5874041837241304]
  This work presents the largest e-commerce product review dataset to date for reviews written in Bengali, English, a mixture of both and Banglish, Bengali words written with English alphabets.<n>The dataset comprises of 1.74 million written reviews from 3.2 million ratings information collected from a total of 128k products being sold in online e-commerce platforms targeting the Bengali population.<n>It includes an extensive array of related metadata for each of the reviews including the rating given by the reviewer, date the review was posted and date of purchase, number of likes, dislikes, response from the seller, images associated with the review etc.
  arXiv  Detail & Related papers  (2024-12-17T18:39:10Z)
• BnSentMix: A Diverse Bengali-English Code-Mixed Dataset for Sentiment Analysis [0.08246494848934446]
  We introduce BnSentMix, a sentiment analysis dataset on code-mixed Bengali consisting of 20,000 samples with 4 sentiment labels from Facebook, YouTube, and e-commerce sites.<n>We achieve an overall accuracy of 69.8% and an F1 score of 69.1% on sentiment classification tasks.
  arXiv  Detail & Related papers  (2024-08-16T18:30:22Z)
• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• From Human Judgements to Predictive Models: Unravelling Acceptability in Code-Mixed Sentences [18.53327811304381]
  Cline is a dataset containing human acceptability for English-Hindi(en-hi) code-mixed text.<n>Cline is the largest judgements of its kind with 16,642 sentences, consisting of samples from two sources: synthetically generated code-mixed text and samples collected from online social media.<n>Our analysis establishes that popular code-mixing metrics such as CMI, Number of Switch Points, Burstines, which are used to filter/curate/compare code-mixed corpora have low correlation with human acceptability, underlining the necessity of our dataset.
  arXiv  Detail & Related papers  (2024-05-09T06:40:39Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• Offensive Language Identification in Transliterated and Code-Mixed Bangla [29.30985521838655]
  In this paper, we explore offensive language identification in texts with transliterations and code-mixing. We introduce TB-OLID, a transliterated Bangla offensive language dataset containing 5,000 manually annotated comments. We train and fine-tune machine learning models on TB-OLID, and we evaluate their results on this dataset.
  arXiv  Detail & Related papers  (2023-11-25T13:27:22Z)
• Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG Evaluation [55.92852268168816]
  N-gram matching-based evaluation metrics, such as BLEU and chrF, are widely utilized across a range of natural language generation (NLG) tasks. Recent studies have revealed a weak correlation between these matching-based metrics and human evaluations. We propose to utilize textitmultiple references to enhance the consistency between these metrics and human evaluations.
  arXiv  Detail & Related papers  (2023-08-06T14:49:26Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• On the Blind Spots of Model-Based Evaluation Metrics for Text Generation [79.01422521024834]
  We explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics. We design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics.
  arXiv  Detail & Related papers  (2022-12-20T06:24:25Z)
• Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling [0.16252563723817934]
  We classify codemixed social media comments/posts in the Dravidian languages of Tamil, Kannada, and Malayalam. A custom dataset is constructed by transliterating all the code-mixed texts into the respective Dravidian language. We fine-tune several recent pretrained language models on the newly constructed dataset.
  arXiv  Detail & Related papers  (2021-08-27T08:43:08Z)
• Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation [98.11249019844281]
  Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation (NAT) models. We propose reverse KD to rejuvenate more alignments for low-frequency target words. Results demonstrate that the proposed approach can significantly and universally improve translation quality.
  arXiv  Detail & Related papers  (2021-06-02T02:41:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.