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Register a new userLeveraging Order-Free Tag Relations for Context-Aware Recommendation
الاستفادة من العلاقات العلامة المجانية للطلب للحصول على توصية السياق
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تعتمد توصية العلامات على وظيفة الترتيب لعلامات Top-K أو طريقة توليد التشغيل التلقائي.ومع ذلك، فإن الطرق السابقة تهمل واحدة من اثنين من الخصائص المتضاربة التي يبدو أنها مرغوبة للغاية لمجموعة العلامة: مناسبا والاعتماد بين الاعتماد.في حين فشل نهج التصنيف في معالجة الاعتماد بين العلامات بين العلامات عندما تكون في المرتبة، فإن النهج التلقائي فشل في اتخاذ أمر في الاعتبار لأنه مصمم لاستخدام العلاقات المتسلسلة بين الرموز.نقترح طريقة توليد تسلسل غبيهة لتوصية العلامات، حيث يتم إنشاء العلامة التالية مستقلة عن ترتيب العلامات التي تم إنشاؤها وترتيب علامات الحقيقة الأرضية التي تحدث في بيانات التدريب.النتائج التجريبية على نطيفين مختلفين، إنستغرام ومكدس تجاوز، تبين أن طريقتنا متفوقة بشكل كبير على النهج السابقة.
Tag recommendation relies on either a ranking function for top-k tags or an autoregressive generation method. However, the previous methods neglect one of two seemingly conflicting yet desirable characteristics of a tag set: orderlessness and inter-dependency. While the ranking approach fails to address the inter-dependency among tags when they are ranked, the autoregressive approach fails to take orderlessness into account because it is designed to utilize sequential relations among tokens. We propose a sequence-oblivious generation method for tag recommendation, in which the next tag to be generated is independent of the order of the generated tags and the order of the ground truth tags occurring in training data. Empirical results on two different domains, Instagram and Stack Overflow, show that our method is significantly superior to the previous approaches.
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