LinkMF: 结合Linked Data的协同过滤推荐算法

协同过滤(CF)是推荐系统中应用最为广泛的推荐算法之一,然而数据稀疏性和冷启动问题是协同过滤方法的两个主要挑战。由于Linked Data整合了关于实体的丰富且结构化的特征,可以作为额外的信息源来缓解以上两种挑战。该文中我们首次提出了结合Linked Data改进CF推荐算法,基于矩阵分解提出了一种新的CF模型——LinkMF,在保证推荐准确度的基础上利用Linked Data缓解数据稀疏性和冷启动问题。首先,我们从Linked Data中抽取项目的特征表示并为项目建模;然后提出新的相似度度量方法计算项目相似度;最后利用项目相似度约束和指导MF分解过程产生推荐。在MovielLens和YAGO标准数据集上的大量实验结果表明,LinkMF优于现有的一些CF方法,特别在缓解数据稀疏性和冷启动问题上取得很好地效果。

LinkMF: Collaborative Filtering Recommendation Algorithm with Linked Data

Collaborative filtering (CF) is one of the most popular recommendation techniques in application. However data sparsity and cold start remain as two challenges in CF applications. Since Linked Data integrates rich and structured features about entities, this paper proposes the idea of leveraging Linked Data to improve CF recommendation algorithm. Based on matrix factorization (MF), we develope a novel CF model denoted as LinkMF, incorporating structured Linked Data to mediate data sparsity and cold start problems while preserving recommendation accuracy. In particular, we extract features from the Linked Data and construct the item profiles; then we propose new similarity metrics for dufferent items; and, finally, we incorporate the obtained item similarities into the basic MF model to constrain and improve the factorization process. Comprehensive experiments on MovieLens and YAGO benchmark datasets demonstrate the promising results of the LinkMF compared with the state-of-the-art CF models, especially in the data sparsity and cold start scenarios.