A transformer-based SASRec deep learning model trained on a 25M-parameter dataset, deployed as a full-stack system with automated data ingestion pipelines. Built as the IIT Ropar capstone project.
Transformer-based sequential recommender with automated data pipelines, built as the IIT Ropar capstone project.
A sequential model, deliberately
SASRec is a self-attentive sequential recommender: it uses attention over a user’s interaction history so that recent and contextually relevant actions carry more weight than old ones. That choice is what makes the model answer "what next" rather than "what similar".
Trained on TensorFlow at 25M parameters
The model is implemented and trained in TensorFlow against a 25M-parameter dataset, and evaluated on a held-out split — the 98.47% AUC-ROC figure is that evaluation, not training performance.
Automated ingestion rather than a fixed dump
Automated data pipelines feed the model instead of a one-off export. A sequential recommender degrades as soon as its view of recent history goes stale, so continuous ingestion is a correctness requirement, not an operational nicety.
Containerised and served, not left in a notebook
Docker packages the training and serving path and a Node.js service exposes it to a React client, which is the difference between a model that scored well and a system someone can call.
Sequence-aware recommendations
Attention over interaction history rather than static similarity.
Automated ingestion
Pipelines keeping the model’s view of history current.
Evaluated, not just trained
98.47% AUC-ROC measured on a held-out split.
Deployable
Containerised and served behind an API.
Model: SASRec transformer
Result: 98.47% AUC-ROC
Context: IIT Ropar capstone
Divyakush Punjabi