【論文筆記】《Interact and decide: Medley of sub-attention networks for effective group recommendation》
SIGIR 2019 A會 模型結構： u、v分別是用戶和項目的隱向量 c是用戶的上下文向量 爲羣組gi中每個用戶分配一個注意力子網絡 以用戶1爲例 其注意力子網絡的輸入是c1和[u2,u3,…,un] 來刻畫用戶1和羣組中其他用戶相互交互的行爲 輸出是用戶2到用戶n的權重 對這些權重做softmax歸一化： 通過加權求和得到用戶1的注意力網絡的輸出gi,1 每個用戶的輸出gi,l再求
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相關文章
- 1. 論文筆記 GAME:Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation
- 2. RKGE Recurrent Knowledge Graph Embedding for Effective Recommendation 論文
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- 8. 論文筆記：Personalized Deep Learning for Tag Recommendation
- 9. 論文筆記：Group Equivariant Convolutional Networks
- 10. 論文筆記11:Development of a Music Recommendation System for Motivating Exercise