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Title:	數位行銷中無Cookie內容對於廣告投遞的產業推薦 Industry Recommendation for Cookieless Contents in Digital Marketing
Authors:	邱盈儒 Chiu, Ying-Ju
Contributors:	李蔡彥 Li, Tsai-Yen 邱盈儒 Chiu, Ying-Ju
Keywords:	Cookieless 數位廣告推薦系統點擊率預測自然語言處理關鍵字萃取 Cookieless Digital Advertising Recommendation System CTR Prediction Natural Language Processing Keyword Extraction
Date:	2024
Issue Date:	2024-09-04 14:35:54 (UTC+8)
Abstract:	隨著各個主流瀏覽器相繼宣布逐步淘汰第三方Cookie，數位廣告投放面臨巨大的挑戰。在遵守全球各國隱私法規的同時，如何仍能保持高精準度的廣告投放，成為數位廣告商的當務之急。本研究提出了一種在不使用任何使用者個人資料的情況下，通過解析使用者瀏覽的文本內容來進行廣告投放的潛在替代方法。我們採用了先進的深度學習技術來處理和理解文本內容。通過對使用者瀏覽的文章、新聞和其他文本內容進行語義分析，我們可以推測出使用者的潛在興趣和需求，進而實現精準的廣告推薦。為了驗證此方法的有效性，我們進行了一系列實驗，重點測試了該方法在點擊率預測任務中的表現。實驗結果顯示，儘管不使用傳統的使用者行為數據，我們的方法仍能達到令人滿意的預測精度。這表明，通過解析文本內容，可以在一定程度上替代Cookie所提供的功能，為數位廣告商提供了一種可行的解決方案。此外，我們還比較了不同深度學習模型和參數配置對預測效果的影響，找出了在不同情境下的最佳配置。本研究不僅為數位廣告投遞提供了一種新的思路，還展示了在無Cookie環境下利用文本內容進行廣告推薦的潛力。隨著數位行銷生態系統的不斷變化，我們的方法有望成為廣告商適應新形勢的重要工具，既能滿足隱私保護的需求，又能保持廣告的高效投放。 As major web browsers gradually announce the phasing out of third-party cookies, digital advertising faces significant challenges. Ensuring high-precision advertisement targeting while complying with global privacy regulations has become a critical issue for digital advertisers. This study proposes a potential alternative method for advertisement delivery without using any personal user data by analyzing the text content browsed by users. We employ advanced deep learning techniques to process and understand the text content. By semantically analyzing articles, news, and other textual content browsed by users, we can infer their potential interests and needs, thereby achieving precise advertisement recommendations. To validate the effectiveness of this method, we conducted a series of experiments, focusing on its performance in the click-through rate (CTR) prediction task. The experimental results show that our method can achieve satisfactory prediction accuracy even without traditional user behavior data. This indicates that text content analysis can partially replace the functionality provided by cookies, offering a feasible solution for digital advertisers. Additionally, we compared the effects of different deep learning models and parameter configurations on prediction performance, identifying the optimal setups under various scenarios. This study not only provides a new approach for digital advertisement delivery but also demonstrates the potential of using text content for advertisement recommendations in a cookieless environment. As the digital marketing ecosystem continues to evolve, our method is expected to become an essential tool for advertisers to adapt to new conditions, meeting privacy protection requirements while maintaining efficient advertisement delivery.
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Description:	碩士國立政治大學資訊科學系碩士在職專班 110971015
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110971015
Data Type:	thesis
Appears in Collections:	[Executive Master Program of Computer Science of NCCU] Theses

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