近期推荐系统冷启动顶会论文集锦

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| 作者：君临

| 链接：https://zhuanlan.zhihu.com/p/439259975

| 来源：知乎

| 整理：机器学习与推荐算法

推荐系统由于新加入的用户或者物品的原因，导致固有的冷启动问题的存在。目前主流的方法是利用除评分信息之外的其他边信息（社交信息、评论信息等）以及利用映射机制、迁移学习等方法来缓解该问题，更多关于冷启动问题的介绍可参考一文梳理冷启动推荐算法模型进展。作者收集了近年来发表在顶级会议SIGKDD、SIGIR、Recsys、IJCAI、AAAI、WWW、ICDM、CIKM以及WSDM上关于缓解推荐系统冷启动的文章，更多关于该方向的论文可以参考以下项目。

https://github.com/hongleizhang/RSPapers

SIGKDD

• Multi-view Denoising Graph Auto-Encoders on Heterogeneous Information Networks for Cold-start Recommendation | Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
  dl.acm.org/doi/10.1145/3447548.3467427
• MAMO | Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  dl.acm.org/doi/10.1145/3394486.3403113
• Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation | Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  dl.acm.org/doi/10.1145/3394486.3403207
• MeLU | Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
  dl.acm.org/doi/10.1145/3292500.3330859

SIGIR

1. Fairness among New Items in Cold Start Recommender Systems | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462948

2. Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462879

3. Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462843

4. FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462831

5. Privileged Graph Distillation for Cold Start Recommendation | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462929

6. Supporting Metacognition during Exploratory Search with the OrgBox | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3462955

7. Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3463033

8. Sequential Recommendation for Cold-start Users with Meta Transitional Learning | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3463089

9. Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

   dl.acm.org/doi/10.1145/3404835.3463010

10. AliMe DA: A Data Augmentation Framework for Question Answering in Cold-start Scenarios | Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3404835.3464923

11. CATN | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401169

12. Content-aware Neural Hashing for Cold-start Recommendation | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401060

13. Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401101

14. AR-CF | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401038

15. DCDIR: A Deep Cross-Domain Recommendation System for Cold Start Users in Insurance Domain | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401193

16. Joint Training Capsule Network for Cold Start Recommendation | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401243

17. A Heterogeneous Graph Neural Model for Cold-start Recommendation | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401252

18. A Heterogeneous Information Network based Cross Domain Insurance Recommendation System for Cold Start Users | Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3397271.3401426

19. Warm Up Cold-start Advertisements | Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

    dl.acm.org/doi/10.1145/3331184.3331268

RecSys

1. Shared Neural Item Representations for Completely Cold Start Problem | Fifteenth ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3460231.3474228

2. Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders | Fifteenth ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3460231.3474252

3. Content-based book recommendations | Fifteenth ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3460231.3474603

4. Siamese Neural Networks for Content-based Cold-Start Music Recommendation. | Fifteenth ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3460231.3478847

5. The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based Inference | Fourteenth ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3383313.3411477

6. Domain adaptation in display advertising | Proceedings of the 13th ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3298689.3347004

7. CB2CF | Proceedings of the 13th ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3298689.3347038

8. Music cold-start and long-tail recommendation | Proceedings of the 13th ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3298689.3347052

9. Trust-based collaborative filtering | Proceedings of the 12th ACM Conference on Recommender Systems

   dl.acm.org/doi/10.1145/3240323.3240404

10. Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations | Proceedings of the Eleventh ACM Conference on Recommender Systems

    dl.acm.org/doi/10.1145/3109859.3109880

IJCAI

1. Preference-Adaptive Meta-Learning for Cold-Start Recommendation | IJCAI

   www.ijcai.org/proceedings/2021/222

2. Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation | IJCAI

   www.ijcai.org/proceedings/2020/379

3. EndCold: An End-to-End Framework for Cold Question Routing in Community Question Answering Services | IJCAI

   www.ijcai.org/proceedings/2020/449

4. Cold-Start Aware Deep Memory Network for Multi-Entity Aspect-Based Sentiment Analysis | IJCAI

   www.ijcai.org/proceedings/2019/722

5. Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness | IJCAI

   www.ijcai.org/proceedings/2017/382

AAAI

1. Cold-start Sequential Recommendation via Meta Learner | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org/index.php/AAAI/article/view/16601

2. Personalized Adaptive Meta Learning for Cold-start User Preference Prediction | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org/index.php/AAAI/article/view/17287

3. Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org//index.php/AAAI/article/view/5360

4. Deeply Fusing Reviews and Contents for Cold Start Users in Cross-Domain Recommendation Systems | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org//index.php/AAAI/article/view/3773

5. HERS: Modeling Influential Contexts with Heterogeneous Relations for Sparse and Cold-Start Recommendation | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org//index.php/AAAI/article/view/4270

6. From Zero-Shot Learning to Cold-Start Recommendation | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org//index.php/AAAI/article/view/4324

7. Low-Rank Linear Cold-Start Recommendation from Social Data | Proceedings of the AAAI Conference on Artificial Intelligence

   ojs.aaai.org/index.php/AAAI/article/view/10758

WWW

1. Task-adaptive Neural Process for User Cold-Start Recommendation | Proceedings of the Web Conference 2021 (acm.org)

   dl.acm.org/doi/10.1145/3442381.3449908

2. Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start | Proceedings of the Web Conference 2021 (acm.org)

   dl.acm.org/doi/10.1145/3442381.3449891

ICDM

1. Fast Adaptation for Cold-Start Collaborative Filtering with Meta-Learning | IEEE Conference Publication | IEEE Xplore

   ieeexplore.ieee.org/document/9338389

2. Cold Item Recommendations via Hierarchical Item2vec | IEEE Conference Publication | IEEE Xplore

   ieeexplore.ieee.org/document/9338322

CIKM

1. Zero Shot on the Cold-Start Problem | Proceedings of the 30th ACM International Conference on Information & Knowledge Management

   dl.acm.org/doi/10.1145/3459637.3482312

2. CMML | Proceedings of the 30th ACM International Conference on Information & Knowledge Management

   dl.acm.org/doi/10.1145/3459637.3482241

3. Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation | Proceedings of the 30th ACM International Conference on Information & Knowledge Management

   dl.acm.org/doi/10.1145/3459637.3482292

4. Dual Autoencoder Network with Swap Reconstruction for Cold-Start Recommendation | Proceedings of the 29th ACM International Conference on Information & Knowledge Management

   dl.acm.org/doi/10.1145/3340531.3412069

5. Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval | Proceedings of the 29th ACM International Conference on Information & Knowledge Management

   dl.acm.org/doi/10.1145/3340531.3412752

6. Semi-Supervised Learning for Cross-Domain Recommendation to Cold-Start Users | Proceedings of the 28th ACM International Conference on Information and Knowledge Management

   dl.acm.org/doi/10.1145/3357384.3357914

7. What You Look Matters? | Proceedings of the 28th ACM International Conference on Information and Knowledge Management

   dl.acm.org/doi/10.1145/3357384.3357813

8. Attention-based Adaptive Model to Unify Warm and Cold Starts Recommendation | Proceedings of the 27th ACM International Conference on Information and Knowledge Management

   dl.acm.org/doi/10.1145/3269206.3271710

WSDM

1. Pre-Training Graph Neural Networks for Cold-Start Users and Items Representation | Proceedings of the 14th ACM International Conference on Web Search and Data Mining

   dl.acm.org/doi/10.1145/3437963.3441738

2. Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph | Proceedings of the 14th ACM International Conference on Web Search and Data Mining

   dl.acm.org/doi/10.1145/3437963.3441773

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