《User Diverse Preference Modeling by Multimodal Attentive Metric Learning》論文閱讀
前言 在該論文中，作者提出了MAML模型，是一種基於多模態信息的度量學習方法，通過該方法學習用戶對於不同物品所具有的不同的偏好特徵。 一、Related Work 首先，論文介紹了相關的工作，主要分爲三個部分：1. 用戶偏好建模；2. 度量學習；3. 多模態。 1. Diverse preference modeling 在第一部分中，作者將前人的相關工作劃分爲兩組： 第一組是利用評論信息來分析每
相關文章
- 1. （論文閱讀筆記）Attentive Aspect Modeling for Review-aware Recommendation
- 2. 論文閱讀:Unsupervised Deep Learning by Neighbourhood Discovery
- 3. 『 論文閱讀』A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
- 4. Machine Learning & Deep Learning 論文閱讀筆記
- 5. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 6. 論文閱讀：Deep Metric Learning via Lifted Structured Feature Embedding
- 7. 論文閱讀理解 - Deep Metric Learning via Lifted Structured Feature Embedding
- 8. 【論文閱讀】Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Ne
- 9. An Attentive Survey of Attention Models 論文閱讀
- 10. 論文閱讀：Diverse Beam Search--Decoding Diverse Solutions from Neural Sequence Models
- 更多相關文章...
- • RSS 閱讀器 - RSS 教程
- • C# 文本文件的讀寫 - C#教程
- • JDK13 GA發佈：5大特性解讀
- • Scala 中文亂碼解決
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. （論文閱讀筆記）Attentive Aspect Modeling for Review-aware Recommendation
- 2. 論文閱讀:Unsupervised Deep Learning by Neighbourhood Discovery
- 3. 『 論文閱讀』A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems
- 4. Machine Learning & Deep Learning 論文閱讀筆記
- 5. 論文閱讀筆記《Attentive Weights Generation for Few Shot Learning via Information Maximization》
- 6. 論文閱讀：Deep Metric Learning via Lifted Structured Feature Embedding
- 7. 論文閱讀理解 - Deep Metric Learning via Lifted Structured Feature Embedding
- 8. 【論文閱讀】Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Ne
- 9. An Attentive Survey of Attention Models 論文閱讀
- 10. 論文閱讀：Diverse Beam Search--Decoding Diverse Solutions from Neural Sequence Models