直播電子商務 (live commerce) 的快速發展，對推薦系統 (RSs) 帶來了新的挑戰，需要建模用戶、商品和主播之間複雜的三方關係，並適當捕捉用戶偏好的變化。本研究提出了一種新的直播電子商務圖形時間感知推薦系統 (LGT-RS) 來應對這些挑戰。LGT-RS 利用圖卷積網絡 (GCNs) 來建模用戶、商品和主播之間的複雜關係，並結合了時間編碼方法來捕捉用戶偏好隨時間的動態演變。此外，為豐富數據並提升模型性能，LGT-RS 融合了一個針對商品、用戶和主播的統一詞嵌入空間。LGT-RS 的有效性通過在台灣直播電商平台真實世界數據集上的大量實驗得到了驗證。結果表明，LGT-RS 在 topK 推薦和鏈接預測任務上的性能優於其他幾個基準模型。本研究通過解決複雜三方關係、快速變化的用戶偏好以及數據集中有限的特徵等挑戰，推進了直播電商推薦系統的發展。

The rapid growth of livestreaming e-commerce (live commerce) poses new challenges for recommender systems (RSs), necessitating the modeling of complex tripartite relationships among users, items, and streamers and capturing user preferences change properly. This study introduces a novel Live commerce Graphical Time-aware Recommender System (LGT-RS) to address these challenges. LGT-RS leverages Graph Convolutional Networks (GCNs) to model the complex relationships between users, items, and streamers, and incorporates a time encoding method to capture the dynamic evolution of user preferences over time. Furthermore, to enhance data richness and improve model performance, LGT-RS integrates a unified word embedding space for items, users, and streamers. The effectiveness of LGT-RS is validated through extensive experiments on a real-world dataset from a Taiwanese live commerce platform. The results demonstrate LGT-RS's superior performance compared to several baseline models in topK recommendation and link prediction tasks.

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