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Personalized Ranking in eCommerce Search
We address the problem of personalization in the context of eCommerce search.
ORDSIM: Ordinal Regression for E-Commerce Query Similarity Prediction
ORDSIM exploits variable-width buckets to model ordinal loss, which penalizes errors in high-level similarity harshly, and thus enable the regression model to obtain better prediction results for high similarity values.
Semantic Equivalence of e-Commerce Queries
Behavioral similarity leverages historical search behavior to generate vector representations of query intent.
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