卷积融合文本和异质信息网络的学术论文推荐算法

针对交互数据稀疏和推荐多样性问题，基于卷积协同过滤推荐框架提出卷积融合文本和异质信息网络的学术论文推荐算法（WN-APR）。首先学习不同语义下用户和论文的多样化特征，缓解数据稀疏问题；然后基于外积设计不同语义特征相互增强的方式融合它们，并使用三维卷积神经网络代替二维卷积神经网络充分挖掘不同特征对性能的影响；最后改进贝叶斯个性化排序损失函数增强推荐多样性。在CiteuLike-a、CiteuLike-t数据集上的实验结果表明，相比于基线模型，WN-APR在准确率和多样性的四个指标上都有所提升。

Convolutional with word and heterogeneous information network for academic paper recommendation

In view of the problems of data sparsity and the diversity in academic paper recommender systems, based on CONV-NCF, 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 3D 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.