Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation
Author:
Affiliation:
1. College of Intelligence and Computing, Tianjin University, China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3604915.3608835
Reference23 articles.
1. Alex Beutel Ed Huai-hsin Chi Zhiyuan Cheng Hubert Pham and John R. Anderson. 2017. Beyond Globally Optimal: Focused Learning for Improved Recommendations. In WWW. 203–212. Alex Beutel Ed Huai-hsin Chi Zhiyuan Cheng Hubert Pham and John R. Anderson. 2017. Beyond Globally Optimal: Focused Learning for Improved Recommendations. In WWW. 203–212.
2. Yukuo Cen Jianwei Zhang Xu Zou Chang Zhou Hongxia Yang and Jie Tang. 2020. Controllable Multi-Interest Framework for Recommendation. In KDD. 2942–2951. Yukuo Cen Jianwei Zhang Xu Zou Chang Zhou Hongxia Yang and Jie Tang. 2020. Controllable Multi-Interest Framework for Recommendation. In KDD. 2942–2951.
3. Zheng Chai Zhihong Chen Chenliang Li Rong Xiao Houyi Li Jiawei Wu Jingxu Chen and Haihong Tang. 2022. User-Aware Multi-Interest Learning for Candidate Matching in Recommenders. In SIGIR. 1326–1335. Zheng Chai Zhihong Chen Chenliang Li Rong Xiao Houyi Li Jiawei Wu Jingxu Chen and Haihong Tang. 2022. User-Aware Multi-Interest Learning for Candidate Matching in Recommenders. In SIGIR. 1326–1335.
4. Gaode Chen Xinghua Zhang Yanyan Zhao Cong Xue and Ji Xiang. 2021. Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation. In IJCAI. 1426–1433. Gaode Chen Xinghua Zhang Yanyan Zhao Cong Xue and Ji Xiang. 2021. Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation. In IJCAI. 1426–1433.
5. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191–198. Paul Covington Jay Adams and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In RecSys. 191–198.
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