HarrieO / OnlineLearningToRank

Related projects ⓘ

Alternatives and complementary repositories for OnlineLearningToRank

• QingyaoAi / Unbiased-Learning-to-Rank-with-Unbiased-Propensity-Estimation
  This is an implementation of the Dual Learning Algorithm with multi-layer feed-forward neural network for online unbiased learning to ran…
  ☆89Updated last year
• HarrieO / 2021-SIGIR-plackett-luce
  ☆33Updated 2 years ago
• acbull / Unbiased_LambdaMart
  Code for WWW'19 "Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm", which is based on LightGBM
  ☆225Updated 5 years ago
• HarrieO / 2021wsdm-unifying-LTR
  ☆19Updated last year
• jiaqima / Off-Policy-2-Stage
  Off-policy Learning in Two-stage Recommender Systems. https://dl.acm.org/doi/pdf/10.1145/3366423.3380130
  ☆28Updated 4 years ago
• hexiangnan / sigir16-eals
  Experiments codes for SIGIR'16 paper "Fast Matrix Factorization for Online Recommendation with Implicit Feedback "
  ☆136Updated 6 years ago
• ashudeep / Fair-PGRank
  Code for Policy Learning for Fairness in Ranking paper at NeurIPS 2019
  ☆20Updated 2 years ago
• ULTR-Community / ULTRA_pytorch
  Unbiased Learning To Rank Algorithms (ULTRA)
  ☆93Updated last year
• hpclab / quickrank
  Official repository of QuickRank: A C++ suite of Learning to Rank algorithms.
  ☆131Updated 5 years ago
• alimamarankgroup / HPMN
  Lifelong sequential modeling for user response prediction. A comprehensive evaluation framework for our SIGIR 2019 paper.
  ☆102Updated 4 years ago
• yihong-chen / lambda-opt
  Pytorch implementation of λOpt: Learn to Regularize Recommender Models in Finer Levels, KDD 2019
  ☆53Updated 4 years ago
• olivierjeunen / dual-bandit-kdd-2020
  Source code for our paper "Joint Policy-Value Learning for Recommendation" published at KDD 2020.
  ☆22Updated last year
• dawenl / expo-mf
  Exposure Matrix Factorization: modeling user exposure in recommendation
  ☆97Updated 8 years ago
• ULTR-Community / ULTRA
  Unbiased Learning To Rank Algorithms (ULTRA)
  ☆287Updated last year
• hpclab / LtR-Tutorial
  Efficiency/Effectiveness Trade-offs in Learning to Rank
  ☆12Updated 6 years ago
• jasonyaw / CDAE
  Collaborative Denoising Auto-Encoder for Top-N Recommender Systems
  ☆110Updated 8 years ago
• sak2km / OnlineLearningToRank
  ☆13Updated 3 years ago
• rank2rec / rerank
  ☆81Updated 5 years ago
• ylongqi / unbiased-offline-recommender-evaluation
  ☆33Updated 6 years ago
• hexiangnan / theano-BPR
  Using theano to implement the Matrix Factorization with BPR ranking loss
  ☆36Updated 8 years ago
• KarypisLab / SLIM
  High-performance implementation of SLIM-based approaches for Top-N recommendation
  ☆111Updated 2 years ago
• lzheng21 / SpectralCF
  ☆51Updated 6 years ago
• hanwangzhang / Discrete-Collaborative-Filtering
  ☆24Updated 8 years ago
• criteo-research / CausE
  Code for the Recsys 2018 paper entitled Causal Embeddings for Recommandation.
  ☆245Updated 6 years ago
• evison / JRL
  Code for paper: Joint Representation Learning for Top-N Recommendation with Heterogenous Information Sources
  ☆49Updated 3 years ago
• dongx-duan / bpr
  ☆42Updated 8 years ago
• graytowne / rank_distill
  A PyTorch implementation of Ranking Distillation
  ☆86Updated 4 years ago
• hamed-zamani / snrm
  Standalone Neural Ranking Model (SNRM)
  ☆74Updated 5 years ago
• evfro / recsys19_hybridsvd
  Accompanying code for reproducing experiments from the HybridSVD paper. Preprint is available at https://arxiv.org/abs/1802.06398.
  ☆25Updated 5 years ago
• rpasricha / TransFM
  ☆83Updated 5 years ago