Ahmed El-Kishky, Thomas Markovich, Kenny Leung, Frank Portman, Aria Haghighi, Ying Xiao. bfkNN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval. In Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng, editors, Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part III. Volume 13937 of Lecture Notes in Computer Science, pages 374-386, Springer, 2023. [doi]
@inproceedings{ElKishkyMLPHX23, title = {bfkNN-Embed: Locally Smoothed Embedding Mixtures for Multi-interest Candidate Retrieval}, author = {Ahmed El-Kishky and Thomas Markovich and Kenny Leung and Frank Portman and Aria Haghighi and Ying Xiao}, year = {2023}, doi = {10.1007/978-3-031-33380-4_29}, url = {https://doi.org/10.1007/978-3-031-33380-4_29}, researchr = {https://researchr.org/publication/ElKishkyMLPHX23}, cites = {0}, citedby = {0}, pages = {374-386}, booktitle = {Advances in Knowledge Discovery and Data Mining - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25-28, 2023, Proceedings, Part III}, editor = {Hisashi Kashima and Tsuyoshi Idé and Wen-Chih Peng}, volume = {13937}, series = {Lecture Notes in Computer Science}, publisher = {Springer}, isbn = {978-3-031-33380-4}, }