Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder.

Xu Zhao|William Gantt Walden|Ying Du|Yi Ren|William Gantt Walden|Ying Du|Dawn J. Lawrie|Shenzheng Zhang|Nian Wang|Dawn J. Lawrie|Shenzheng Zhang


Anthology ID:DBLP:conf/sigir/ZhaoRDZW22
Volume:SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022
Year:2022
Venue:Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
Publisher:ACM
Pages:2595-2600
URL:https://doi.org/10.1145/3477495.3531902
DOI:https://doi.org/10.1145/3477495.3531902
DBLP:conf/sigir/ZhaoRDZW22
BibTeX:
@inproceedings{zhao-2022-improving, author = {Xu Zhao and William Gantt Walden and Ying Du and Yi Ren and William Gantt Walden and Ying Du and Dawn J. Lawrie and Shenzheng Zhang and Nian Wang and Dawn J. Lawrie and Shenzheng Zhang}, editor = {Enrique Amig\'{o} and Pablo Castells and Julio Gonzalo and Ben Carterette and J. Shane Culpepper and Gabriella Kazai}, title = {{Improving Item Cold-start Recommendation via Model-agnostic Conditional Variational Autoencoder}}, booktitle = {{SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022}}, pages = {2595--2600}, publisher = {ACM}, year = {2022}, url = {https://doi.org/10.1145/3477495.3531902}, doi = {https://doi.org/10.1145/3477495.3531902} }