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Register a new userNeural News Recommendation with Collaborative News Encoding and Structural User Encoding
توصية الأخبار العصبية مع ترميز الأخبار التعاونية وترميز المستخدم الهيكلي
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اكتسبت توصية الأخبار التلقائية الكثير من الاهتمام من المجتمع الأكاديمي والصناعة. تكشف الدراسات الحديثة أن مفتاح هذه المهمة يكمن في تعلم التمثيل الفعال في كل من الأخبار والمستخدمين. تعمل الأعمال الحالية عادة عنوان الأخبار والمحتوى بشكل منفصل مع إهمال تفاعلها الدلالي، وهو غير كاف من أجل فهم نص الأخبار. إلى جانب ذلك، ترميز النماذج السابقة سجل تصفح المستخدم دون الاستفادة من الارتباط الهيكلي لأخبار استعراض المستخدمين لتعكس اهتمامات المستخدم صراحة. في هذا العمل، نقترح إطار توصية أخبار يتكون من ترميز الأخبار التعاونية (CNE) وترميز المستخدم الهيكلية (SUE) لتعزيز تعلم الأخبار وتمثيل المستخدم. CNE مجهزة LSTMS ثنائي الاتجاه ترميز عنوان الأخبار والمحتوى التعاوني مع الوحدات النمطية الشاملة والاهتمام لمعرفة تمثيل الأخبار الدلالية التفاعلية. تستخدم SUE الشبكات التنافسية الرسمية لاستخراج السمات الهيكلية الكتلة لسجل المستخدم، تليها وحدات الانتباه بين الكتلة والإنتباه إلى التعلم لتعلم تمثيلات فائدة المستخدم الهرمية. نتائج التجربة على DataSet العقل التحقق من صحة فعالية نموذجنا لتحسين أداء توصية الأخبار.
Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective representation learning of both news and users. Existing works typically encode news title and content separately while neglecting their semantic interaction, which is inadequate for news text comprehension. Besides, previous models encode user browsing history without leveraging the structural correlation of user browsed news to reflect user interests explicitly. In this work, we propose a news recommendation framework consisting of collaborative news encoding (CNE) and structural user encoding (SUE) to enhance news and user representation learning. CNE equipped with bidirectional LSTMs encodes news title and content collaboratively with cross-selection and cross-attention modules to learn semantic-interactive news representations. SUE utilizes graph convolutional networks to extract cluster-structural features of user history, followed by intra-cluster and inter-cluster attention modules to learn hierarchical user interest representations. Experiment results on the MIND dataset validate the effectiveness of our model to improve the performance of news recommendation.
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