在推薦系統領域中，神經網絡的使用越來越普遍，在過去大部分的研究當中通常透過單一表徵來代表使用者的整體偏好，而在一些研究當中注意到使用者偏好可以被解釋為使用者的多方面的興趣，因此多興趣的概念在推薦系統設計中引起了越來越多的關注。通常這些多興趣框架會使用注意力機制來提取多興趣的表徵。然而，透過注意力機制的多興趣表示無法清楚地展示使用者的各方面興趣。另外它們也忽略了隱藏的細微特徵和使用者歷史序列中不同項目之間的關係。因此，我們提出了一種新的方法，命名為Fine-grained Feature Extraction via Pixel Attention and Distinctive Interest Learning (FEPADI)。其利用額外的注意力網絡來捕捉細微特徵。此外，FEPADI還利用了對比學習來增強使用者興趣表徵的差異性。通過增強使用者興趣表徵，我們可以獲得更準確和詳細的使用者偏好，從而實現更有效的推薦。我們也在數個真實世界資料集上進行了實驗，並將我們的方法與最先進的方法進行了比較。實驗結果顯示，與比較對象相比FEPADI取得了更加優異的表現，並且在額外的實驗中也驗證了FEPADI中每個模塊的有效性。

Recently, the use of neural networks in the recommender system field has been increasing year by year, and a single embedding is generally used to represent the overall preference of a user. Some researchers further noticed that user preference can also be interpreted as multiple aspects of user interests, and the concept of multi-interests attracts more and more attention in the design of recommender systems. Commonly, those multi-interest frameworks use the attention mechanism to extract multi-interest representations. However, the multi-interest representations obtained by those works cannot clearly show distinguishable aspects of user interests. Moreover, they usually ignore the fine-grained feature hidden in the items and the relationship between different items in the user history sequence. Therefore, we propose a novel method called FEPADI, which leverages an additional attention network for capturing the fine-grained feature. Additionally, contrastive learning is utilized in FEPADI to enhance the distinctiveness of the user interest representations. With the enhancement of the user interest representations, we can obtain more accurate and detailed user preferences, leading to more effective recommendations. We conducted experiments on the real-world datasets and compared our performance with that of state-of-the-art sequential, contrastive learning and multi-interest frameworks and several baseline recommender methods. Experimental results demonstrated the significant improvements of FEPADI compared with the competitors and validated the effectiveness of each module in the proposed FEPADI.

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