构造层次化的category树状结构，然后再构造相关的矩阵，利用这个矩阵以及原来的隐因子矩阵，共同重构隐因子矩阵，最后利用重构后的矩阵，运用在原始的矩阵分解当中。
| 文献题目 | 去谷歌学术搜索 | ||||||||||
| Learning Hierarchical Category Influence on both Users and Items for Effective Recommendation | |||||||||||
| 文献作者 | Zhu Sun; Jie Zhang | ||||||||||
| 文献发表年限 | 2017 | ||||||||||
| 文献关键字 | |||||||||||
| category information | |||||||||||
| 摘要描述 | |||||||||||
| Item category has proven to be useful additional informa- tion to address the data sparsity and cold start problems in recommender systems. Although categories have been well studied in which they are independent and structured in a flat form, in many real applications, item category is often organized in a richer knowledge structure - category hierar- chy, to reflect the inherent correlations among different cate- gories. In this paper, we propose a novel latent factor model by exploiting category hierarchy from the perspectives of both users and items for effective recommendation. Specifi- cally, a user can be influenced by her preferred categories in the hierarchy. Similarly, an item can be characterized by the associated categories in the hierarchy. We incorporate the influence that different categories have towards a user and an item in the hierarchical structure. Experimental results on two real-world data sets demonstrate that our method consistently outperforms the state-of-the-art category-aware recommendation algorithms. | |||||||||||
