Cold-Start-RecSys

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Papers, Codes, and Frameworks are used to solve cold-start problem in Recommendation System

This repository contains a list of papers, codes, and Frameworks are used to solve cold-start problem in Recommendation System. If you found any error, please don't hesitate to open an issue or pull request.

Paper & codes

1. Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation SIGIR 2020 [pdf] [code]
2. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation KDD 2020  [pdf] [code]
3. MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation KDD 2020 [pdf] [code]
4. How to Learn Item Representation for Cold-Start Multimedia Recommendation? MM'2020 [pdf] [code]
5. Attribute Graph Neural Networks for Strict Cold Start Recommendation TKDE 2020 [pdf] [code]
6. Task-adaptive Neural Process for User Cold-Start Recommendation WWW 2021 [pdf] [code]
7. Content-aware Neural Hashing for Cold-start Recommendation SIGIR20 [pdf] [code]
8. Learning Attribute-to-Feature Mappings for Cold-Start Recommendations ICDM 2010 [pdf] [code]
9. HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-start Recommendation AAAI19 [pdf] [code]
10. CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network SIGIR2020 [pdf] [code1] [code2]
11. Improved Cold-Start Recommendation via Two-Level Bandit Algorithms BRACIS 2017 [pdf] [code]
12. DropoutNet: Addressing Cold Start in Recommender Systems NeurIPS'17 [pdf] [code]
13. Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph WSDM2021 [pdf] [code]
14. Music cold-start and long-tail recommendation: bias in deep representations Recsys19 [pdf] [code]
15. x x [pdf] [code]
16. x x [pdf] [code]
17. x x [pdf] [code]

upcoming

5. Sequential Recommendation for Cold-start Users with Meta Transitional Learning SIGIR2021 [pdf] [code]
6. Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks SIGIR2021 [pdf] [code]
7. Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users SIGIR2021 [pdf] [code]
8. Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction SIGIR2021 [pdf] [code]
9. FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation SIGIR2021 [pdf] [code]
10. Privileged Graph Distillation for Cold-start Recommendation SIGIR2021 [pdf] [code]
11. Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users SIGIR2021 [pdf] [code]
12. Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction SIGIR2021 [pdf] [code]

Contributor

Contributed by Juyong Jiang and Peilin Zhou.