implicit-feedbacks

Repository for the Recommender Systems Challenge 2020/2021 @ PoliMi

The objective of the competition was to create the best recommender system for a book recommendation service by providing 10 recommended books to each user. The evaluation metric was MAP@10.

Assignment Recommendation System using Collaborative Filtering for Implicit Feedbacks This project provides APIs for recommendation of assignments for a user based on implicit feedbacks and recommendation of assignments based on implicit relations using Machine Learning

Recommender System 2019 Challenge PoliMi

Two-stage book recommender over 103K titles: multi-source retrieval (ALS, item-kNN, BM25, sentence embeddings) → LightGBM LambdaMART ranking. Temporal split, cold-start routing, FastAPI + Docker, GCP/Vertex scaffolding.