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Register a new userGeo-BERT Pre-training Model for Query Rewriting in POI Search
Geo-Bert Pre-Training Model لإعادة كتابة الاستعلام في بحث POI
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يقترح إعادة كتابة الاستعلام (QR) حل مشكلة عدم تطابق الكلمة بين الاستفسارات والمستندات في البحث على الويب. الأساليب الحالية عادة ما نموذج QR مع نموذج تسلسل نهاية إلى نهاية (SEQ2SEQ). يمكن أن تتعلم النماذج القائمة على المحولات الحديثة بفعالية دلالات نصية من سجلات جلسة المستخدم، لكنها غالبا ما تتجاهل معلومات الموقع الجغرافي للمستخدمين الحيوية لتحقيق البحث عن نقطة الفائدة (POI) على خدمات الخريطة. في هذه الورقة، اقترحنا نموذجا ما قبل التدريب، يسمى GEO-BERT، لدمج الدلالات والمعلومات الجغرافية في التمثيلات المدربة مسبقا للويس. أولا، نحاكي توزيع POI في العالم الحقيقي كشركة رسم بياني، حيث تمثل العقد Pois ومتعدد الحبيبات الجغرافية. ثم نستخدم أساليب تعلم التمثيل الرسم البياني للحصول على تمثيلات جغرافية. أخيرا، نحن ندرب نموذجا ما قبل التدريب يشبه بيرت مع تضيير الرسوم البيانية النصية والنصية للحصول على تمثيل متكامل لكل من المعلومات الجغرافية والدلية، وتطبيقه في البحث عن QR of POI. يحقق النموذج المقترح دقة ممتازة على مجموعة واسعة من مجموعات بيانات خريطة العالم الواقعية.
Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search. Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model. The state-of-the-art Transformer-based models can effectively learn textual semantics from user session logs, but they often ignore users' geographic location information that is crucial for the Point-of-Interest (POI) search of map services. In this paper, we proposed a pre-training model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs. Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities. Then we use graph representation learning methods to get geographic representations. Finally, we train a BERT-like pre-training model with text and POIs' graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search. The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.
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