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Register a new userQuery2Prod2Vec: Grounded Word Embeddings for eCommerce
Query2Prod2VEC: Ended Word Embeddings للتجارة الإلكترونية
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نقدم Query2Prod2VEC، وهو نموذج يسبب تمثيلات معجمية للبحث عن المنتج في تضمين المنتج: في نموذجنا، يعني المعنى رسم خرائط بين الكلمات والمساحة الكامنة من المنتجات في متجر رقمي.نستفيد من جلسات التسوق لتعلم المساحة الأساسية واستخدام التعليقات التوضيحية للتجارة لبناء التظليلات المعجمية للتقييم: تظهر تجاربنا أن طرازنا أكثر دقة من التقنيات المعروفة من أدب NLP و IR.أخيرا، نشدد على أهمية كفاءة البيانات للبحث عن المنتج خارج عمالقة البيع بالتجزئة، وتسليط الضوء على كيفية تناسب Query2Prod2VEC قيودا عملية التي يواجهها معظم الممارسين.
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop. We leverage shopping sessions to learn the underlying space and use merchandising annotations to build lexical analogies for evaluation: our experiments show that our model is more accurate than known techniques from the NLP and IR literature. Finally, we stress the importance of data efficiency for product search outside of retail giants, and highlight how Query2Prod2Vec fits with practical constraints faced by most practitioners.
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