基于Word2vec和卷积神经网络的新闻推荐算法

Proceedings of the 6th International Conference on Virtual and Augmented Reality Simulations Pub Date : 2022-03-25 DOI:10.1145/3546607.3546622

Zhengqi Ding, Chang Sun, Gang Sun, Qihang Liu, Zhi-wei Ma

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引用次数: 0

摘要

新闻的信息过载使得用户很难找到他们感兴趣的新闻。如何从数以万计的新闻中获取用户感兴趣的新闻，成为当前新闻推荐领域的迫切需要。因此，本文提出了一种基于Word2vec和卷积神经网络的新闻推荐算法。首先对新闻内容进行建模，利用Word2vec对新闻词向量模型进行训练，然后利用卷积神经网络模型对新闻进行分类;其次，对用户兴趣进行建模，得到用户新闻话题偏好矩阵;最后，采用基于用户新闻主题偏好矩阵的协同过滤算法进行新闻推荐。实验表明，基于Word2vec和卷积神经网络的新闻推荐算法具有较好的推荐性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A News Recommendation Algorithm Based on Word2vec and Convolutional Neural Network

The information overload of news makes it difficult for users to find news they are interested in. How to obtain the news that users are interested in among tens of thousands of news has become an urgent need in the current news recommendation field. Therefore, this paper proposes a news recommendation algorithm based on Word2vec and convolutional neural network. Firstly, the news content is modeled, and Word2vec is used to train the news word vector model, and then a convolutional neural network model is used to classify news; secondly, the user interest is modeled to obtain a user-news topic preference matrix; finally, a collaborative filtering algorithm is used to recommend news based on the user-news topic preference matrix. The experiments show that the news recommendation algorithm based on Word2vec and convolutional neural network has better recommendation performance.

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Proceedings of the 6th International Conference on Virtual and Augmented Reality Simulations

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