在使用者偏好這一不斷變化的領域內，順序推薦系統已被證實具有至關重要的作用。 這些系統能夠利用用戶過去與平台或產品的互動數據，以提供即時、個人化的推薦。 為了滿足這種不斷演化的需求，我們提出了一個全新的機制－時間和變化感知機制用於增強順序推薦（Time and Variation-Aware Mechanism for Enhanced Sequential Recommendation，簡稱TVaR）。 TVaR 機制在設計上精妙而優雅，但其前提卻非常簡單：僅透過分析使用者的交易歷史來提煉和優化推薦結果。 這在技術實施上相當直接，也意味著該機制具有很高的實用性和可擴展性。 到目前為止，據我們所知，我們是第一組採用變分自編碼器（Variational Autoencoder，VAE）來識別物品中的潛在因子，並將這些因子與時間戳嵌入相結合的研究人員。這種結合方法具有突破性意義，因為它無縫地整合了資料中固有的時間和潛在維度，從而更全面地理解了使用者的行為和偏好。 為了進一步提升系統效能，我們也採用了資料增強技術，以強化雙向 Transformer 模型的表現。實證研究的結果對 TVaR 的優越性提供了有力支持。 我們發現，當使用 VAE 產生的嵌入和時間嵌入與雙向 Transformer 模型進行精心整合後，系統的效能表現有了顯著提升。 這項發現為該領域設定了新的、更高的基準。 TVaR 不僅在單一的測試資料集中表現出色，而且一貫地超越了目前最先進的推薦系統方法。 證明了 TVaR 的通用性和長期應用的潛力。 整體而言，TVaR 透過其精妙的設計和高度的實用性，為順序推薦系統領域帶來了新的可能性和觀點。 它成功地解決了多個核心問題，包括如何更準確地捕捉使用者偏好，如何有效地整合時間和潛在訊息，以及如何透過資料增強來提高模型效能。 因此，TVaR 構成了一個值得進一步研究和應用的框架。

In the dynamic realm of user preferences, sequential recommendation systems have proven essential, leveraging past interactions to provide real-time, personalized suggestions. In response to this evolving need, we present the Time and Variation-Aware Mechanism for Enhanced Sequential Recommendation (TVaR). This mechanism, elegantly designed yet simple in its premise, refines recommendations by solely capitalizing on user transaction histories. To our knowledge, we are the first to deploy a Variational Autoencoder (VAE) to discern latent factors from items and subsequently meld these with timestamp embeddings. This approach seamlessly encapsulates both the temporal and latent dimensions inherent in the data. To further bolster its efficacy, data augmentation techniques are employed, fortifying the bidirectional-Transformer model's performance. Empirical evidence supports TVaR's prominence, revealing that when VAE embeddings and Time embeddings are harmoniously paired with the bidirectional-Transformer model, a notable enhancement in performance is observed, setting a new, refined benchmark in the domain. Significantly, TVaR consistently surpasses prevailing state-of-the-art methodologies.

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