Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation
Unsupervised Learning of Paragraph Embeddings for Context-Aware Recommendation
复制标题
用于上下文感知推荐的段落嵌入的无监督学习
DOI：
10.1109/access.2019.2906659
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Xiong, Wei
中科院分区：
文献类型：
--
作者：
Xie, Jin;Zhu, Fuxi;Xiong, Wei
数据的稀疏性是制约推荐系统性能的主要原因之一。为了解决稀疏性问题，一些推荐系统使用辅助信息，特别是文本信息，作为补充，以提高预测精度的评级。然而，这两种基于文本分析的主流方法都存在一定的局限性。基于词袋的模型就是其中之一，它难以有效地利用段落的上下文信息，只能对段落进行粗浅的理解。另一种基于深度学习的模型可以提取段落的上下文信息，但也增加了模型的复杂性。提出了一种新的上下文感知推荐模型--段落向量矩阵分解（P2 VMF），它将段落嵌入的无监督学习与概率矩阵分解（PMF）相结合。因此，P2 VMF能够捕捉到段落的语义信息，提高评分的预测精度。我们在真实数据集上的大量实验表明，P2 VMF模型的性能优于那些多推荐模型的情况下，其中的评级是相当稀疏。并验证了模型中的P2 V部分能够很好地以向量的形式表达语义。
The sparsity of data is one of the main reasons restricting the performance of recommender systems. In order to solve the sparsity problem, some recommender systems use auxiliary information, especially text information, as a supplement to increase the prediction accuracy of the ratings. However, the two mainstream approaches based on text analysis have some limitations. The bag-of-words-based model is one of them, being difficult to use the contextual information of the paragraph effectively so that only the shallow understanding of paragraph can be parsed. Another model based on deep learning can extract the contextual information of the paragraph, but it also increases the complexity of the model. This paper proposes a novel context-aware recommendation model named paragraph vector matrix factorization (P2VMF) which integrates the unsupervised learning of paragraph embeddings into probabilistic matrix factorization (PMF). Therefore, P2VMF can capture the semantic information of the paragraph and can improve the prediction accuracy of the ratings. Our extensive experiments on real-world datasets show that the performance of the P2VMF model is preferable as compared with those multiple recommendation models in the situation, where the ratings are quite sparse. And we also verified that the P2V part of the model can well express the semantics in the form of vectors.