复杂网络上基于多维特征表示学习的推荐算法

网络表示学习可以有效解决推荐面临的数据稀疏问题.本文对网络表示学习中LINE算法和DeepWalk算法进行改进,提出混合推荐算法并应用于电影推荐场景.该算法通过学习用户喜好特征、厌恶特征和相似用户特征,生成三个低维特征向量.将三个低维特征向量线性组合拼接成用户表示向量,以余弦相似度作为相似性指标,将相似用户关联的电影推荐给目标用户,实现电影推荐.实验结果表明,所提出的推荐算法相较于次优算法,在MovieLens数据集上的准确率和F1指标分别提升12%和7%,在MovieTweetings数据集上的准确率和F1指标分别提升16%和18%.本文提出的基于多维特征表示学习的推荐算法在电影推荐类场景中,具有显著的优越性.

Recommendation Algorithm Based on Multi-dimensional Feature Representation Learning in Complex Networks

Network representation learning can effectively solve the problem of data sparsity in recommendation. In this paper, LINE and DeepWalk in network representation learning were improved, and a hybrid recommendation algorithm was proposed to be applied to movie recommendation scene. The new algorithm generates three low dimensional feature vectors by learning user preference feature, user aversion feature and similar user feature. Three low dimensional feature vectors are linearly combined to form a user representation vector, and cosine similarity is used as the similarity index to recommend the movies associated with similar users to target users. Experimental results show that, compared with the suboptimal algorithm, the accuracy and F1 index of the proposed algorithm are improved by 12% and 7% respectively on MovieLens dataset, and 16% and 18% respectively on MovieTweetings dataset. The recommendation algorithm based on multi-dimensional feature representation learning proposed in this paper has significant advantages in movie recommendation scenes.