ruizhang-ai / SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-SystemsLinks
Detecting Beneficial Feature Interactions for Recommender Systems, AAAI 2021
☆34Updated 3 years ago
Alternatives and similar repositories for SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems
Users that are interested in SIGN-Detecting-Beneficial-Feature-Interactions-for-Recommender-Systems are comparing it to the libraries listed below
Sorting:
- Code for the SIGIR20 paper -- Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation☆43Updated 5 years ago
- [TKDE 2021] Source code and datasets for the paper "Personalizing Graph Neural Networks with Attention Mechanism for Session-based Recomm…☆83Updated last year
- Codes for papers: 1. Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters (ICML). 2. Less is More: Explorin…☆78Updated last year
- This is the official pytorch implementation of AutoDebias, an automatic debiasing method for recommendation.☆108Updated 2 years ago
- The implementation of SIGIR 2020 paper "CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network“, Cheng Zhao,…☆46Updated 4 years ago
- Code for GroupIM: A Mutual Information Maximization Framework for Neural Group Recommendation (SIGIR 2020)☆49Updated 2 years ago
- TensorFlow implementation of our paper: Cross Pairwise Ranking for Unbiased Item Recommendation (WWW'22)☆43Updated last year
- GMCF: Neural Graph Matching based Collaborative Filtering, SIGIR 2021☆54Updated 3 years ago
- This is our implementation of EHCF: Efficient Heterogeneous Collaborative Filtering (AAAI 2020)☆106Updated 5 years ago
- Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback☆28Updated 4 years ago
- The official implementation of "Disentangling Long and Short-Term Interests for Recommendation" (WWW '22)☆81Updated 2 years ago
- Source code for KDD 2020 paper "Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation"☆125Updated 2 years ago
- [KDD 2021] Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System☆76Updated 3 years ago
- TensorFlow implementation for paper Time Interval Aware Self-Attention for Sequential Recommendation.☆127Updated 4 years ago
- Released code of SIGIR2021 Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer.☆51Updated 3 years ago
- [ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."☆45Updated last year
- This is an implementation for our SIGIR 2021 paper "Causal Intervention for Leveraging Popularity Bias inRecommendation" based on tensorf…☆105Updated 2 years ago
- ☆32Updated 4 years ago
- This is our implementation of ENMF: Efficient Neural Matrix Factorization (TOIS. 38, 2020). This also provides a fair evaluation of exist…☆153Updated 3 years ago
- ☆34Updated 4 years ago
- Incorporating User Micro-behaviors and Item Knowledge 59 60 3 into Multi-task Learning for Session-based Recommendation☆45Updated 5 years ago
- Official implementation of SIGIR'2021 paper: "Sequential Recommendation with Graph Neural Networks".☆85Updated 2 years ago
- The code for paper MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation☆53Updated 4 years ago
- The official implementation of "Disentangling User Interest and Conformity for Recommendation with Causal Embedding" (WWW '21)☆155Updated last year
- [WWW 2021]Task-adaptive Neural Process for User Cold-Start Recommendation☆33Updated 4 years ago
- This repo includes some graph-based CTR prediction models and other representative baselines.☆68Updated last year
- Handling Information Loss of Graph Neural Networks for Session-based Recommendation☆67Updated 3 years ago
- Code for Next-item Recommendation with Sequential Hypergraph (SIGIR2020)☆59Updated 4 years ago
- ☆172Updated 3 years ago
- Code for WSDM 2022 paper, Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation.☆106Updated 3 years ago