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We aim to proceed to improve the mannequin to achieve higher accuracy by utilizing numerous methods, similar to date refinement of product title, effectivity of picture characteristic extraction, and accurate verification set constructing. We have mentioned the category auto-matching mannequin operated in NAVER Shopping as well as related problems and solutions.First, we found useful options from merchandise, analyzed morphemes within the case of textual content information, and used CNN and LSTM models. For picture information, we used MobileNet model to develop the category auto-matching mannequin. The classes of NAVER Shopping are structured for user convenience, and it’s exhausting to tell apart the upper categories because of many decrease classes of the same title. Since it is not an excellent construction to allow the model to learn, it’s essential to either choose categories requiring further studying to alter to a construction that’s easy for the mannequin to study, or manage the standard categories (e.g. UNSPSC, The United Nations Standard Products & Services Code) within the commerce surroundings individually and let it study.
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