Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks.

@inproceedings{zhu-2021-learning, author = {Yongchun Zhu and Ruobing Xie and Fuzhen Zhuang and Kaikai Ge and Ying Sun and Xu Zhang and Leyu Lin and Juan Cao}, editor = {Fernando Diaz and Chirag Shah and Torsten Suel and Pablo Castells and Rosie Jones and Tetsuya Sakai}, title = {{Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks}}, booktitle = {{SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual Event, Canada, July 11-15, 2021}}, pages = {1167--1176}, publisher = {ACM}, year = {2021}, url = {https://doi.org/10.1145/3404835.3462843}, doi = {https://doi.org/10.1145/3404835.3462843} }