Sequential Recommendation with User Memory Networks簡介
Sequential Recommendation with User Memory Networks Abstract 存在的推薦系統算法總是將用戶歷史記錄embed成一個單一隱式表示，這可能會失去一些特徵或物品的關聯性。因此作者提出記憶增強的神經網絡與協同過濾的思想進行整合從而作出推薦。通過藉助外部的記憶矩陣，作者可以存儲並更新用戶的歷史記錄，這有效的提高了模型的表達能力。 Introduct
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相關文章
- 1. Memory Augmented Graph Neural Networks for Sequential Recommendation翻譯
- 2. GNN:Session-based Recommendation with Graph Neural Networks簡介
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- 4. 序列推薦模型梳理（Sequential Recommendation）
- 5. Visual Question Answering with Memory-Augmented Networks
- 6. Sequential Scenario-Specific Meta Learner for Online Recommendation簡析
- 7. Next Item Recommendation with Self-Attention簡介
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- 9. 2020上半年的sequential recommendation論文整理01——概述
- 10. Graph Neural Networks for Social Recommendation