TIM-REC: 基於時間間隔建模探討下一個籃子推薦系統之應用

Abstract

下一個購物籃推薦系統 (NBR) 因其在電子商務和雜貨購物的應用中產生重大影響。下一個購物籃推薦系統的目的是根據使用者的購買記錄來預測用戶的下一籃商品。 傳統的推薦方法（例如基於頻率和基於鄰居的方法）已被廣泛研究，但往往忽略了連續籃子之間的時間間隔變化。深度學習的最新進展引進了新的方法，可以更有效地為使用者行為建模。然而，這些模型主要著重於籃子的順序，而不考慮籃子之間的時間間隔，使推薦系統的表現受到限制。 我們的研究提出了一種新模型，稱為時間間隔建模的下一個購物籃推薦模型（Temporal Interval Modeling for Next Basket Recommendation, TIM-REC），將時間間隔信息整合到推薦過程中。為了評估 TIM-REC 是否能夠有效捕捉購物籃之間的時間關聯性，我們在三個真實世界的資料集上進行了大量實驗，結果證明 TIM-REC 與其他方法相比的有效性。

Next Basket Recommendation (NBR) systems have garnered significant interest due to their practical applications in e-commerce and grocery shopping. These systems aim to predict a user's next basket of items based on their purchase history, which is a set of items frequently bought or consumed together. Traditional recommendation methods, such as frequency-based and neighbor-based approaches, have been extensively studied but often overlook the temporal dynamics between consecutive baskets. Recent advancements in deep learning have introduced new methodologies to model user behaviors more effectively. However, these models primarily focus on the sequence of baskets without considering the time intervals between them. Our work introduces a novel approach, Temporal Interval Modeling for Next Basket Recommendation (TIM-REC), which integrates temporal interval information into the recommendation process. To evaluate the performance of TIM-REC, we conducted extensive experiments on three real-world datasets. Our results demonstrate that TIM-REC outperforms other state-of-the-art deep learning models by effectively capturing the temporal dependencies between baskets.