DST approach on Recommended Systems(RS).
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Updated
Sep 12, 2023 - Python
DST approach on Recommended Systems(RS).
Implementation of the paper - Neural Collaborative Filtering
GitSum is a novel approach to the summarization of README.MD, it helps automatically fill the blank “About” field for GitHub repositories. It is built on top of BART and T5, fine-tuning on existing data to perform recommendations for repositories with a missing description.
A movie recommendation engine
Implementation of model-based and memory-based collaborative filtering to predict movie rating
User-based recommender system with complete SVD implementation
Consistent Optimization of Label-wise Utilities in Multi-label classificatioN
CS422 - Introduction to data mining
A repository to practice with recommendation engines.
Adaptive Hierarchical Attention-Enhanced Gated Network Integrating Reviews for Item Recommendation [TKDE 2021]
A webapp for recommending movies based on two models: collaborative filtering with non-negative matrix factorisation and k-nearest neighbours algorithm.
[arXiv] Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations
My graduation project for Computer Engineering Department.
LightFM convenience tools.
A PyTorch implementation of GCCF
Recommendation engine wrapped in Flask (based on 27,225,144 ratings and MovieLens dataset)
A repo for implementing, understanding and analyzing recommender systems in Python.
Taxonomy for Recommender Systems
Recommender System built using Python, Angular, Firebase & MySQL
A generalizable collaborative filtering approach for recommending new procedures to patients and their caregivers.
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