Abstract:
According to the reading habits of users, news recommendations can push more suitable content for them, but the existing methods still have the problem of insufficient feature learning. To solve this problem, a method based on multi-channel CNN-BiGRU and multi-feature fusion is proposed, which is mainly composed of the following four parts: (1) Word Embedding Layer. Entity embedding vectors are integrated into word vectors to make up for the deficiency of using word embedding vectors and construct multi-channel word vectors; (2) Multi-channel CNN-BiGRU Model. In the model, a convolutional neural network (CNN) is used to extract the local features of sentences, and a bidirectional gated recurrent unit (BiGRU) is used to extract the long sequence dependency of text. At the same time, the maximum pool algorithm is used to reduce the number of parameters and avoid redundancy, and the attention network is used to pay attention to the features of key words to extract accurate features; (3) Multi-feature Fusion. With the help of attention mechanism, integrate multiple news features and pay attention to important features to complete the construction of news representation; (4) User Encoder. The interaction of news in the browsing histories of a user is extracted by multi-headed self-attention mechanism to construct user representation and complete recommendation.The experimental results show that compared with CNN, DKN, TANR, and NRMS models, CBMR performs better in AUC, MRR, nDCG@5, and nDCG@10 indicators.