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Register a new userLeveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search
الاستفادة من الرسوم البيانية من المزايدة للمعلن الأهمية ذات الصلة في البحث برعاية
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في الآونة الأخيرة، أصبح البحث برعاية واحدة من أكثر القنوات المربحة للتسويق. كأساس أساسي للبحث المدعى عليه، اجتذبت النمذجة ذات الصلة الاهتمام المتزايد بسبب القيمة العملية الهائلة. معظم الطرق الحالية تعتمد فقط على أزواج الكلمات الرئيسية للاستعلام. ومع ذلك، عادة ما تكون الكلمات الرئيسية عادة نصوص قصيرة مع معلومات دلالية ندرة، والتي قد لا تعكس بدقة النوايا الإعلانية الأساسية. في هذه الورقة، نقوم بالتحقيق في مشكلة الرواية في النمذجة ذات الصلة بالمعلن، والتي ترفف معلومات المعلنين لسد الفجوة بين نوبة البحث وأغراض الإعلان. يكمن دوافعنا في دمج سلوكيات العطاءات غير المزودة بحيث تكون الرسوم البيانية التكميلية لتعلم تمثيلات معلنة مرغوبة. قد نقترح مزيدا من الرسوم البيانية المزايدة في الرسم البياني المعزز بنموذج BGTR مع ثلاثة أبراج لصمامات الرسوم البيانية العطاءات والبيانات النصية الدلالية. تجريبيا، نقوم بتقييم نموذج BGTR عبر مجموعة بيانات كبيرة، والنتائج التجريبية تظهر باستمرار تفوقها.
Recently, sponsored search has become one of the most lucrative channels for marketing. As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value. Most existing methods solely rely on the query-keyword pairs. However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents. In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers' information to bridge the gap between the search intents and advertising purposes. Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. We further propose a Bidding-Graph augmented Triple-based Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.
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