基于知识图谱和标签感知的推荐算法

Recommendation Algorithm Based on Knowledge Graph and Tag-aware

推荐系统缓解了互联网数据量剧增带来的信息过载问题,但传统的推荐系统由于数据稀疏和冷启动等问题导致推荐算法的准确性不高。因此,文中提出了一种基于知识图谱和标签感知的推荐算法(Knowledge Graph and Tag-Aware,KGTA)。首先,利用项目和用户标签信息,通过知识图谱表示学习捕获低阶与高阶特征,将两个知识图谱中实体和关系的语义信息嵌入低维的向量空间中,从而获得项目和用户的统一表示。其次,分别利用深度神经网络和加入注意力机制的递归神经网络来提取项目和用户的潜在特征。最后,根据潜在特征预测评分。该算法不仅利用了知识图谱和标签的关系信息和语义信息,而且通过深层结构学习了项目和用户的隐含特征。在MovieLens数据集上的实验结果表明,该算法能够有效预测用户评分,提高推荐结果的准确性。

Recommendation systems alleviate the problem of information overload caused by the rapid increase of data on the Internet.But traditional recommendation systems are not accurate enough due to data sparsity and cold start.Therefore,a novel recommendation algorithm based on knowledge graph and tag-aware(KGTA)is proposed.First,tags of items and users are used to capture low-order and high-order features through knowledge graph representation learning.The semantic information of entities and relationships in two knowledge graphs is embedded into a low-dimension vector space to obtain the unified representation of items and users.Then,deep neural networks and recurrent neural networks combining attention mechanism are respectively utilized to extract the latent features of items and users.Finally,ratings are predicted on the basis of latent features.KGTA not only takes relationship information and semantic information of knowledge graph and tags into consideration,but also learns latent features of items and users through deep structures.Experimental results on MovieLens datasets illustrate that the proposed algorithm performs better in rating prediction and improves the accuracy of recommendation.