活動流與圖分析在電商網站行銷科技之應用

近年隱私權意識高漲，歐盟祭出GDPR法規，強化個資當事人權益，Google宣布將取消支援第三方cookies，行銷科技受到限縮。然而電子商務市場持續成長，電商網站必須在隱私權的限制下，追蹤使用者行為，尋求任何可以改善使用者體驗的可能。因此，本研究捨棄所有使用者特徵並去識別化，僅蒐集用戶與網站內各區塊間的互動，開發使用者行為分析系統。相較其他系統以大量使用者個資做為分析依據，本研究將使用者的活動流進行瀏覽歷程識別，並將之視為圖形資料以進行圖分析(graph analytics)。我們使用圖形相似性為基礎的多種連結預測(link prediction)方法與圖神經網路(graph neural networks)分析使用者與網站元件的互動。對外提供消費者即時商品推薦，對內則提供網站架構設計與頁面布局重構建議。本研究蒐集實際電商網站之使用者紀錄進行實驗，結果顯示透過連結預測演算法進行商品推薦，Top5推薦可得43.40%的平均命中率。利用圖神經網路進行區塊分析，預測區塊間的關聯性，Top5推薦可得72.05%的平均命中率。我們接著提出濾除「雜訊操作」的方法，可將Top5推薦的平均命中率再提升4.06%。

關鍵字

使用者行為；活動流；圖神經網路；連結預測；推薦系統

並列摘要

In recent years, the awareness of privacy is rising where the EU proposed GDPR to strengthen the rights of individual data parties, and Google announced to cancel the support for third-party cookies. Conventional MarTech(marketing technology) is restricted. However, the e-commerce continues to grow, and e-commerce websites must track user behavior and seek any possibility to improve the user experience within the constraints of privacy. Therefore, this thesis discards all user characteristics and de-identifies them, and only collects the interaction between users and various blocks in the website. Compared with other systems that use a large amount of users’ personal data as the basis for analysis, this thesis did session identification on users’ activity streams they were treated as graph data and uses a variety of link prediction algorithms based on graph similarity and graph neural networks to analyze user interactions with website elements. It provides consumers with real-time product recommendations, and provides website architecture design and page layout reconstruction suggestions to website maintainers. This thesis collects user records of actual online e-commerce websites and conducts experiments. The results show that through one of the link prediction methods for product recommendation, the Top5 recommendation can achieve an average hit rate of 43.40%. Using graph neural network to analyze blocks and predict the correlation between blocks, the average hit rate of the Top5 recommendation is 72.05%. Then, We proposed a method to filter out "noise operations", which can increase the average hit rate of the Top5 recommendation by 4.06%.

並列關鍵字

user behavior ； activity stream ； graph neural network ； link prediction ； recommendation system

參考文獻

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