A product based recommendation system which uses Machine learning algorithm such as KNN and cosine similarity and also uses MongoDB as a database which stores the user data for a semi-collaborative filtering. Accuracy : Calculated accuracy using nDCG. Some randomly selected product efficiency: Batman killer croc takedown figures: nDCG=0.917 Star Wars Movie Heroes Yoda: nDCG=0.942 Harry Potter Hogwarts Bookmarks: nDCG=0.9406 Technology Used in this project: Pandas Numpy Sklearn MongoDB as Databases Streamlit for UI Demo: Home UI: Database structure: Result […]

Dataset used: Microsoft News Dataset is a huge dataset for newsrecommendation research.It was collected from anonymousbehavior logs of Microsoft News website.The purpose ofMIND is to serve as a benchmark dataset for newsrecommendation and facilitate the research in newsrecommendation and recommender systems area.MIND contains about 160k English news articles and morethan 15 million impression logs generated by 1 millionusers.We randomly sampled 1 million users who had at least5 news click records during 6 weeks from October 12 toNovember 22, 2019. Every […]

@author: misajie@update: 20220123 File organization: RecEnv ClassicalRL OfflineRL In progress: Classical off-policy models construction and application on existing environments (Recsim, Virtual Taobao) Reconstruct simulator-free model, eg. feedrec Modify Recsim to fit Wechat short video dataset and run off-policy models and evaluate the result Generate reply samples from short video recommendation environment Build classical offline models Build original offline model Evaluate new model add autoML GitHub View Github    

We consider how a user of a web service can build their own recommender system. Many recommender systems on the Internet are still unfair/undesirable for some users, in which case the users need to leave the service or unwillingly continue to use the system. Our proposed concept, private recommender systems, provides a way for the users to resolve this dilemma. Paper: https://arxiv.org/abs/2105.12353 💿 Dependency $ pip install -r requirements.txt $ sudo apt install wget unzip 🗃️ Download and Preprocess Datasets […]

There are three arguments here. It saves the recommendations in zipped pickle file (dictionary format { customer : list_of_recommended_products }). You can use the relevance scores estimated in your dataset in csv format (rows: customers, columns: producers) for your application scenario. Alternatively you can test with ours. The relevance scores calculated for the datasets (used in the paper) can be found in the following links in zipped csv format. If you use this repository in your research, please cite the […]

This is the official implementation of our AAAI’21 paper: Haoran Xin, Xinjiang Lu, Tong Xu, Hao Liu, Jingjing Gu, Dejing Dou, Hui Xiong, Out-of-Town Recommendation with Travel Intention Modeling, In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI’21), Online, 2021, 4529-4536. both PaddlePaddle and Pytorch versions are provided. PaddlePaddle: https://www.paddlepaddle.org.cn Pytorch: https://pytorch.org If you use our codes in your research, please cite: @inproceedings{xin2021out, title={Out-of-Town Recommendation with Travel Intention Modeling}, author={Xin, Haoran    

This repository contains a framework for Recommender Systems (RecSys), allowing users to choose a dataset on a model based on their demand. CAPRI Overview ☑️ Prerequisites You will need below libraries to be installed before running the application: Python >= 3.4 NumPy >= 1.19 SciPy >= 1.6 PyInquirer >= 1.0.3 For a simple solution, you can simply run the below command in the root directory: pip install -r prerequisites.txt 🚀 Launch the Application Start the project by running the main.py […]