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لا تفوت العملاء المحتملين!استرداد إعلانات مماثلة لتحسين استهداف المستخدم
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استهداف المستخدم مهمة أساسية في صناعة الإعلانات الحديثة: بالنظر إلى حزمة من الإعلانات للحصول على فئة معينة من المنتجات (على سبيل المثال، الشاي الأخضر)، حدد المستخدمين عبر الإنترنت الذين يجب استهداف حزمة الإعلانات.عادة ما يتم تدريب نموذج استهداف المستخدم (حزمة الإعلانية) استخدام بيانات النقر التاريخية: تتوافق المثيلات الإيجابية للمستخدمين الذين نقروا في إعلان في الحزمة من قبل، في حين تتوافق الحالات السلبية للمستخدمين الذين لم ينقرون على أي إعلانات في الحزمة التيعرضت لهم.ومع ذلك، فإن جمع كمية كافية من بيانات التدريب الإيجابية لتدريب نموذج استهداف مستخدم دقيق، ومع ذلك، ليس بأي حال من الأحوال تافهة.تركز هذه الورقة على تطوير طريقة للتموة التلقائية لمجموعة حالات التدريب الإيجابية.النتائج التجريبية على مجموعة بيانات اثنين، بما في ذلك مجموعة بيانات حقيقية لشركة العالم، توضح فعالية طريقة لدينا المقترحة.
User targeting is an essential task in the modern advertising industry: given a package of ads for a particular category of products (e.g., green tea), identify the online users to whom the ad package should be targeted. A (ad package specific) user targeting model is typically trained using historical clickthrough data: positive instances correspond to users who have clicked on an ad in the package before, whereas negative instances correspond to users who have not clicked on any ads in the package that were displayed to them. Collecting a sufficient amount of positive training data for training an accurate user targeting model, however, is by no means trivial. This paper focuses on the development of a method for automatic augmentation of the set of positive training instances. Experimental results on two datasets, including a real-world company dataset, demonstrate the effectiveness of our proposed method.
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