錯誤！文檔中沒有指定樣式的文字。PAGE6總結與展望全文總結在本畢業設計的論文中，本研究主要完成了將圖譜數據利用GNN模型實現推薦任務這一課題。本文介紹了相關研究成果以及相關領域中的先驗知識，然后基于這些前人的成果，提出了基于Tucker分解增強的GAT物品推薦模型，通過引入知識表示問題中的Tucker分解，使得模型GAT模型在處理數據的過程中能夠更有效地利用圖譜中的關系信息，實現對于關系的建模與特征提取。本研究所做的主要工作包括以下幾點：實現全數據CKG圖的Tucker分解，得到這些CKG的嵌入表示，并用這些表示作為后續模型的初始化值。利用Tucker分解的核心公式，將上文中初始化的節點表示與核心矩陣表示巧妙地融入注意力值的計算中，從而實現了對于關系信息的建模與提取，節點的匯聚不光與節點本身相關，也與它們相連的邊有關。構建新的分數計算公式，從而將Tucker分解和GNN側模型的訓練統一到一個框架下，實現兩者的交替訓練和實時更新，使得這兩者在這個問題中能夠更加有針對性地更新參數。構建完整的實驗代碼，利用DGL開源框架，快速實現各種GNN模型，對比實驗結果，說明本方法的有效性。調整優化所有實驗模型的參數，嘗試不同的正則化方式與采樣方式。未來展望本主要是圍繞如何利用圖譜數據進行推薦任務而展開的，利用GNN是因為它在圖結構數據上具有很好的表現，但并非一定要采用GNN的方法。通過上文的實驗可以看到GNN方法在這類問題上確實有比較強的能力，但是GNN的缺點也是很明顯的：一是只有借助更新的開源框架，例如DGL等才能實現模型的快速迭代與構建；二是雖然這些框架對GNN的運行做了優化，但依然無法避免GNN訓練耗費大量空間與時間的事實，分子圖batch進行訓練的GNN方法還不夠成熟與完善，因此在更加大的圖結構與數據上想要應用GNN方法還比較吃力。因此，本文所采用的方法相較于工業落地的實用性而言還有一段距離；同時上文提到的采樣方法雖然有一定效果但也非常的樸素，這也是后面可以改進的一個方向。建立更加簡單有效的模型，解決GNN耗費資源過大的問題，例如使用邊采樣或節點采樣等方式，都是本研究可以繼續深入挖掘的亮點。參考文獻Zhou,Jie,etal."Graphneuralnetworks:Areviewofmethodsandapplications."

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