PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation

Eli Chien, Jiong Zhang, Cho-Jui Hsieh, Jyun-Yu Jiang, Wei-Cheng Chang, Olgica Milenkovic, Hsiang-Fu Yu
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:5616-5630, 2023.

Abstract

The eXtreme Multi-label Classification (XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data augmentation method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a $\sim 5$% gain in accuracy on the largest dataset LF-AmazonTitles-1.3M.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-chien23a, title = {{PINA}: Leveraging Side Information in e{X}treme Multi-label Classification via Predicted Instance Neighborhood Aggregation}, author = {Chien, Eli and Zhang, Jiong and Hsieh, Cho-Jui and Jiang, Jyun-Yu and Chang, Wei-Cheng and Milenkovic, Olgica and Yu, Hsiang-Fu}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {5616--5630}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/chien23a/chien23a.pdf}, url = {https://proceedings.mlr.press/v202/chien23a.html}, abstract = {The eXtreme Multi-label Classification (XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data augmentation method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a $\sim 5$% gain in accuracy on the largest dataset LF-AmazonTitles-1.3M.} }
Endnote
%0 Conference Paper %T PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation %A Eli Chien %A Jiong Zhang %A Cho-Jui Hsieh %A Jyun-Yu Jiang %A Wei-Cheng Chang %A Olgica Milenkovic %A Hsiang-Fu Yu %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-chien23a %I PMLR %P 5616--5630 %U https://proceedings.mlr.press/v202/chien23a.html %V 202 %X The eXtreme Multi-label Classification (XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query text. However, conventional XMC studies usually neglect the side information of instances and labels, which can be of use in many real-world applications such as recommendation systems and e-commerce product search. We propose Predicted Instance Neighborhood Aggregation (PINA), a data augmentation method for the general XMC problem that leverages beneficial side information. Unlike most existing XMC frameworks that treat labels and input instances as featureless indicators and independent entries, PINA extracts information from the label metadata and the correlations among training instances. Extensive experimental results demonstrate the consistent gain of PINA on various XMC tasks compared to the state-of-the-art methods: PINA offers a gain in accuracy compared to standard XR-Transformers on five public benchmark datasets. Moreover, PINA achieves a $\sim 5$% gain in accuracy on the largest dataset LF-AmazonTitles-1.3M.
APA
Chien, E., Zhang, J., Hsieh, C., Jiang, J., Chang, W., Milenkovic, O. & Yu, H.. (2023). PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:5616-5630 Available from https://proceedings.mlr.press/v202/chien23a.html.

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