Recommendation System using ML and DL
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Updated
Dec 8, 2022 - Jupyter Notebook
Recommendation System using ML and DL
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
Developed recommendation pipelines leveraging content-based and collaborative filtering to present top n customer recommendations from user items and customer purchase histories. Alternatively, image similarity recommendations were generated using k means clustering and Neural Networks (NNs) from product images.
The Hybrid Movie Recommender is a system that recommends movies using a combination of collaborative and content-based filtering techniques. The system is designed to address the cold start problem(new users) by using a popularity based approach. The dataset used for the system is obtained from Kaggle.
Implementation of various recommendation algorithms such as Collaborative filtering, SVD and CUR-decomposition to predict user movie ratings
To answer which items are frequently bought together we will be using Apriori & FPgrowth Algorithm
A concise guide exploring techniques for building accurate and engaging book recommendation systems, catering to diverse reader preferences.
Recommendation_Systems
This repository contains our teaemwork in the context of the "Information Retrieval (IR)" course (held at FUM) projects.
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