（論文閱讀筆記）Attentive Aspect Modeling for Review-aware Recommendation
論文：Attentive Aspect Modeling for Review-aware Recommendation 1.解決問題 由於用戶與產品互動的稀疏性以及用戶使用多種語言的原因，在針對目標用戶和產品的評論中提到的常見Aspect的數量通常非常有限，存在Aspect稀疏性。 對於不同的產品（即使在同一類別中），用戶關注的方面可能有所不同。 2.創新點 1.對同義和相似的aspect進行交
相關文章
- 1. 【論文閱讀】Gated Attentive-Autoencoder for Content-Aware Recommendation
- 2. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》論文閱讀及解析
- 3. 【論文閱讀筆記】Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment
- 4. 論文閱讀02——WWW2020 Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
- 5. 論文閱讀03——Future Data Helps Training- Modeling Future Contexts for Session-based Recommendation
- 6. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 7. 論文筆記：NAIS: Neural Attentive Item Similarity Model for Recommendation
- 8. 論文筆記 GAME:Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation
- 9. Attention-based LSTM for Aspect-level Sentiment Classification論文閱讀筆記
- 10. 《Group-Buying Recommendation for Social E-Commerce》論文閱讀筆記
- 更多相關文章...
- • RSS 閱讀器 - RSS 教程
- • PHP 實例 - AJAX RSS 閱讀器 - PHP教程
- • Tomcat學習筆記（史上最全tomcat學習筆記）
- • JDK13 GA發佈：5大特性解讀
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 【論文閱讀】Gated Attentive-Autoencoder for Content-Aware Recommendation
- 2. 《NAIS: Neural Attentive Item Similarity Model for Recommendation》論文閱讀及解析
- 3. 【論文閱讀筆記】Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment
- 4. 論文閱讀02——WWW2020 Intention Modeling from Ordered and Unordered Facets for Sequential Recommendation
- 5. 論文閱讀03——Future Data Helps Training- Modeling Future Contexts for Session-based Recommendation
- 6. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 7. 論文筆記：NAIS: Neural Attentive Item Similarity Model for Recommendation
- 8. 論文筆記 GAME:Learning Graphical and Attentive Multi-view Embeddings for Occasional Group Recommendation
- 9. Attention-based LSTM for Aspect-level Sentiment Classification論文閱讀筆記
- 10. 《Group-Buying Recommendation for Social E-Commerce》論文閱讀筆記