Scope of Recommendation Systems in India for engineering students

Where Recommendation Systems skills lead

Graduates applying Recommendation Systems skills typically target roles such as Machine Learning Engineer, Data Scientist, Search / Ranking Engineer, Personalisation Engineer, Analytics Engineer. Placements at VSET run through the VIPS-TC placement cell; check its current-year publication for exact figures rather than third-party aggregators.

How VSET teaches Recommendation Systems

Recommendation systems predict which items a user is likely to want, using collaborative filtering, content features or learned embeddings. They power feeds, search ranking and e-commerce personalisation. At VSET this maps to elective-level coverage inside B.Tech CSE (AI & DS).

• The AI & Data Science track at VSET is the natural home for recommendation work, being one of the seven GGSIPU-affiliated B.Tech programmes.
• Embeddings and vector similarity — the core mechanism of modern recommenders — are documented in the curriculum at learn.engineering.vips.edu through the vector database and RAG material.
• Machine learning and deep learning foundations in the same curriculum supply the modelling methods.
• Recommenders are not published as a separate library topic, so coverage is best described as elective.

What students actually build

• Recommendation projects fit the applied ML capstone pattern alongside CV and NLP tools.
• Personalisation builds are a recurring hackathon category, including at the Smart India Hackathon.