论文笔记——Fine-grained Interest Matching for Neural News Recommendation

本文介绍了FIM模型在新闻推荐系统中的应用,该模型通过多级别用户兴趣表征和卷积神经网络提高兴趣匹配精度。FIM包括News Representation Model、Cross Interaction Model和Click Predictor Model三个部分,利用Hierarchical Dilated Convolution编码新闻特征,并通过交叉乘积和3D CNN捕捉用户与新闻的匹配向量,从而更准确地预测用户点击行为。

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Abstract

本篇论文是针对个人新闻推荐系统再提出了一个新的模型——FIM(同样基于神经网络)

  • 核心问题: 用户的兴趣候选新闻二者的精确匹配
  • 现有模型存在的问题: 现存的模型都会选择将用户的兴趣表征为一个单独的向量,这样会使用户的很多兴趣表征缺失
  • FIM模型的基本构造思路: 不同于之前的模型将用户的兴趣表征为一个联合的向量,FIM模型使用多级别的用户兴趣表征方式使用卷积神经网络来提取用户的多方面的兴趣。

Approach

问题的建模

个人新闻推荐问题可以表述如下,给定用户 u,以及用户 u 的点击新闻历史 Su,然后给定候选新闻 Ci,候选新闻还带有标签 Yi,根据以上给出的数据来求一个模型 g,来通过给出一个用户 u 以及其历史点击新闻 Su 来预测用户是否会点击候选新闻。

Model View

FIM模型分为三部分,News Representation Model、Cross Interaction Model以及Click Predictor Model,分别为用于新闻表示的模型,交叉乘积的模型以及预测评分的模型

Object detection in remote sensing images is a challenging task due to the complex backgrounds, diverse object shapes and sizes, and varying imaging conditions. To address these challenges, fine-grained feature enhancement can be employed to improve object detection accuracy. Fine-grained feature enhancement is a technique that extracts and enhances features at multiple scales and resolutions to capture fine details of objects. This technique includes two main steps: feature extraction and feature enhancement. In the feature extraction step, convolutional neural networks (CNNs) are used to extract features from the input image. The extracted features are then fed into a feature enhancement module, which enhances the features by incorporating contextual information and fine-grained details. The feature enhancement module employs a multi-scale feature fusion technique to combine features at different scales and resolutions. This technique helps to capture fine details of objects and improve the accuracy of object detection. To evaluate the effectiveness of fine-grained feature enhancement for object detection in remote sensing images, experiments were conducted on two datasets: the NWPU-RESISC45 dataset and the DOTA dataset. The experimental results demonstrate that fine-grained feature enhancement can significantly improve the accuracy of object detection in remote sensing images. The proposed method outperforms state-of-the-art object detection methods on both datasets. In conclusion, fine-grained feature enhancement is an effective technique to improve the accuracy of object detection in remote sensing images. This technique can be applied to a wide range of applications, such as urban planning, disaster management, and environmental monitoring.
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