DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search

• URL: http://arxiv.org/abs/2104.11760v1
• Date: Fri, 23 Apr 2021 18:04:44 GMT
• Title: DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search
• Authors: Ali Ahmadvand, Surya Kallumadi, Faizan Javed, and Eugene Agichtein
• Abstract summary: We propose a deep learning model, DeepCAT, which learns joint word-category representations to enhance the query understanding process. Our results show that DeepCAT reaches a 10% improvement on em minority classes and a 7.1% improvement on em tail queries over a state-of-the-art label embedding model.
• Score: 15.041444067591007
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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., \textit{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 \textit{minority} classes, \textit{tail} and \textit{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 {\em minority} categories and {\em tail} queries, we conduct two sets of experiments. The results show that DeepCAT reaches a 10\% 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.

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