Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e.g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding. To address these problems, we propose a deep learning model, DeepCAT, which learns joint word-category representations to enhance the query understanding process. We believe learning category interactions helps to improve the performance of category mapping on minority classes,tail andtorso queries. DeepCAT contains a novel word-category representation model that trains the category representations based on word-category co-occurrences in the training set. The category representation is then leveraged to introduce a new loss function to estimate the category-category co-occurrences for refining joint word-category embeddings. To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% minority categories and%DIFDELCMD < {\em %%% tail queries, we conduct two sets of experiments. The results show that DeepCAT reaches a 10 %DIFDELCMD < \\%%% %DIF < improvement on {\em minority} classes and a 7.1\\% improvement on {\em tail} queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.abstract 
Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e.g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding. To address these problems, we propose a deep learning model, DeepCAT, which learns joint word-category representations to enhance the query understanding process. We believe learning category interactions helps to improve the performance of category mapping on minority classes, tail and torso queries. DeepCAT contains a novel word-category representation model that trains the category representations based on word-category co-occurrences in the training set. The category representation is then leveraged to introduce a new loss function to estimate the category-category co-occurrences for refining joint word-category embeddings. To demonstrate our model's effectiveness on %DIFDELCMD < {\em %%% %DIFDELCMD < {\em %%% minority categories and tail queries, we conduct two sets of experiments. The results show that DeepCAT reaches a 10 %DIFDELCMD < \\%%% %DIF < improvement on {\em minority} classes and a 7.1\\% improvement on {\em tail} queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.abstract \% improvement on minority classes and a 7.1\% improvement on tail queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.
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tail
tail
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minority
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classes,
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tail
D
and
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torso
minority classes, tail and torso
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minority
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categories and
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tail
minority categories and tail
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\% improvement on minority classes and a 7.1\% improvement on tail queries over a state-of-the-art label embedding model. Our findings suggest a promising direction for improving e-commerce search by semantic modeling of taxonomy hierarchies.