This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.

A pytorch implementation for BPR (Bayesian Personalized Ranking).

Bayesian Personalized Ranking using PyTorch

BPR recommender system

Bayesian Personalized Ranking in Python

Predicting missing pairwise preferences from similarity features in group decision making and group recommendation system

Set of recommender systems

A personality-aware group recommendation system based on pairwise preferences

Comparing Exploitation-Based and Game Theory Optimal Based Approaches in a Multi-Agent Environment (2020 Spring)

Bayesian Personalized Ranking is a learning algorithm for collaborative filtering first introduced in: BPR: Bayesian Personalized Ranking from Implicit Feedback. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner and Lars Schmidt-Thieme, Proc. UAI 2009.

Unofficial Implementation of BPRH: Bayesian personalized ranking for heterogeneous implicit feedback

This repository contains the code for the paper "A Methodology for the Offline Evaluation of RecommenderSystems in a User Interface with Multiple Carousels", published at UMAP Late-Breaking Results 2021.

Tensorflow implementation of PRIS (Personalized Ranking with Importance Sampling. WWW 2020)

A newly interpreted code of C++ project `SMORe`, which developed in Python to enhance the usage-flexibility and migration-potential.

BSI-PT algorithm in the paper "Opponent Exploitation Based on Bayesian Strategy Inference and Policy Tracking"

[YOLOv2/YOLOv3/YOLOv5]Tool used to generate anchor-boxes required for training YOLO networks