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Register a new user\#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention
\ #howyoutagtweets: تعلم المستخدم التفضيلات التفضيلات الخاصة بالمستخدم عبر موضوع موضوعي
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يتم إنشاء ملايين علامة التجزئة على وسائل التواصل الاجتماعي كل يوم لإرشاد الرسائل المتعلقة بموضوعات مماثلة. لمساعدة الناس على العثور على الموضوعات التي يرغبون في مناقشتها، تقوم هذه الورقة بتفضيل تفضيلات Hashtaginging للمستخدم عبر التنبؤ بمدى المرجح أن ينشرها مع علامة التجزئة. يتم افتراض أنه يرتبط اهتمامات المرء في علامة التجزئة بما قالها قبل (سجل المستخدم) ووظائف المشاركات الحالية التي تقدم علامة التجزئة (سياقات Hashtag). هذه العوامل متزوجة في المساحة الدلالية العميقة التي بنيت برت مدرب مسبقا ونموذج موضوع عصبي عبر التعلم المتعدد. وبهذه الطريقة، يمكن تخصيص اهتمامات المستخدم المستفادة من الماضي لتتناسب مع علامة التصنيف المستقبلية التي تتجاوز قدرة الأساليب الموجودة على افتراض أن دلالات البحث في هاشتاج دون تغيير. علاوة على ذلك، نقترح انتباه موضوع شخصي مخصص رواية لالتقاط محتويات بارزة لتخصيص سياقات HASHTAG. تشير التجارب على مجموعة بيانات Twitter واسعة النطاق إلى أن نموذجنا يتفوق بشكل كبير على نهج توصية الحديث عن الفن دون استغلال موضوعات كامنة.
Millions of hashtags are created on social media every day to cross-refer messages concerning similar topics. To help people find the topics they want to discuss, this paper characterizes a user's hashtagging preferences via predicting how likely they will post with a hashtag. It is hypothesized that one's interests in a hashtag are related with what they said before (user history) and the existing posts present the hashtag (hashtag contexts). These factors are married in the deep semantic space built with a pre-trained BERT and a neural topic model via multitask learning. In this way, user interests learned from the past can be customized to match future hashtags, which is beyond the capability of existing methods assuming unchanged hashtag semantics. Furthermore, we propose a novel personalized topic attention to capture salient contents to personalize hashtag contexts. Experiments on a large-scale Twitter dataset show that our model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics.
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