Abstract

• Extreme classification deals with problem with extremely large number of labels of the order O(106).

• The SwiftXML algorithm is developed to tackle such warm-start applications by leveraging label features.

• SwiftXML improves upon the state-of-the-art tree based extreme classifiers by partitioning tree nodes using two hyperplanes learnt jointly in the label and data point feature spaces.

Introduction

• Existing Extreme Classification methods do not leverage label feature data.

• Existing warm start methods do not scale to extreme settings.

Materials and Methods

Conclusions

• Can leverage item features which can provide rich, and complementary, source of information to the user features relied on by traditional extreme classifiers.

• Improvement over existing warm start classifiers as well as XML classifiers and scale to large number of labels.

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Industrial Significance• Applications in Ads, tagging and recommendation etc.• In a live deployment for sponsored search on Bing,

SwiftXML could increase the relative CTR by 10 % while simultaneously reducing the BR by 30 %.

Technology Readiness Level Ready!

(Y. Prabhu, A. Kag, S. Gopinath, K. Dahiya, S. Harsola, R. Agrawal and M. Varma*)

Kunal Dahiya (Ph.D. Scholar)

Experiments/Results

• Word2Vec for label features.

• Up to 14% acc-urate predictions

• Performs better than early/late fusion.

Bing Sponsored Search DeploymentSwiftXML predictions generated up to 8% higher Click through rates and 9% lower bounce rates.

Industry Day Theme #3: Artificial Intelligence, Machine

Learning and Blockchain Technologies (AMB)

Label Feature Data (Z)

Users

Labels (Items)

Rating Matrix (R)

User Feature Data (X)

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User Feature Space Item Feature Space

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Node

𝑚𝑖𝑛𝑤𝑥,𝛿,𝑟± ||𝑤𝑥||1 + ||𝑤𝑧||1 + 𝐶Σ𝑖 log 1 + 𝑒−𝛿(𝛼𝑤𝑥

𝑡𝑥𝑖+ 1−𝛼 𝑤𝑧𝑡𝑧𝑖)

−𝐶𝑟 Σ𝑖ℒ𝑛𝐷𝐶𝐺@𝐿 𝑟𝛿𝑖 , 𝑦𝑖

𝑤𝑥 𝜖𝑅𝐷𝑥 , 𝑤𝑧 𝜖𝑅

𝐷𝑧, 𝛿𝜖 −1,+1 𝐿 , 𝑟+, 𝑟−𝜖𝜋(1, 𝐿)

𝑤𝑥 User feature hyperplane𝑤𝑧 Item feature hyperplane𝛿 Partition assignment of users

𝑟+, 𝑟− Label ranking in each partition

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20% Revealed 40% Revealed 60% Revealed 80% Revealed

Precision@5 for Wiki10 dataset

WRMF PfastreXML SLEEC PDSparse DiSMEC IMC SwiftXML

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20% Revealed 40% Revealed 60% Revealed 80% Revealed

Precision@5 for Wikipedia-500K dataset

PfastreXML PfastreXML-early PfastreXML-late SwiftXML

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20% Revealed 40% Revealed 60% Revealed 80% Revealed

Precision@5 for Wikipedia-500K dataset

PfastreXML SwiftXML

User Feature Data

train data

test data

Rating Matrix

N = 20, 40, 60, 80

Evaluation on datasets

Algorithm Relative CTR Relative QOA Relative BR

Bing-ensemble 100 100 100

PfastreXML 102 103 76

SwiftXML 110 112 69

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References

• Extreme multi-label learning with label features for warm-start tagging, ranking and recommendation. In WSDM 2018.

• Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications. In KDD 2016.

• Distributed Representations of Words and Phrases and their Compositionality. In CoRR 2013

• DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification, In WSDM 2017.

AcknowledgementJoint work with Y. Prabhu, A. Kag, S. Gopinath, S. Harsola, R. Agrawal and M. Varma*

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Precision@5 for Amazon-670K dataset

PfastreXML SLEEC DiSMEC SwiftXML