151. 卷积融合文本和异质信息网络的 学术论文推荐算法.
- Author
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吴俊超, 刘柏嵩, 沈小烽, and 张雪垣
- Subjects
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INFORMATION networks , *CONVOLUTIONAL neural networks , *MACHINE learning , *PRODUCT design , *ALGORITHMS - Abstract
In view of the problems of data sparsity and the diversity in academic paper recom-mender systems,based on CONVNCF, this paper proposed an algorithm of convolution with word and heterogeneous information network for academic paper recommendation ( WN -APR) . Firstly, WN -APR algorithm learned user and paper' s diverse features from different semantics to alleviate the sparsity problem. Then it designed an outer product fusing way to seamlessly combine user features with paper features. Replacing of 2D CNN, this algorithm applied 3 D convolution to mine the influence of different features on the performance. Finally, it modified the BPR loss function to enhance diversity in recommendations. Experimental results on CiteULike-a and CiteULike-t datasets show that WN-APR improves the performance of accuracy and diversity over the baseline models. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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