Collaborative Item Embedding Model for Implicit Feedback Data
Collaborative Item Embedding Model for Implicit Feedback Data
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DOI：
10.1007/978-3-319-60131-1_19
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
ThaiBinh Nguyen;K. Aihara;A. Takasu
中科院分区：
文献类型：
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作者：
ThaiBinh Nguyen;K. Aihara;A. Takasu
协同过滤是推荐系统中最常用的方法。执行协同过滤的一种方法是矩阵分解，它使用隐向量来表征用户偏好和项目属性。这些潜在向量善于捕捉用户和项目的全局特征，但在捕捉用户之间或项目之间的局部关系方面不强。在这项工作中，我们提出了一种方法来提取项目之间的关系，并将它们嵌入到因子分解模型的潜在向量。这结合了两个世界：矩阵分解用于协同过滤和项目嵌入，这是一个类似于语言处理中的单词嵌入的概念。我们在三个真实数据集上的实验表明，我们提出的方法在top-nrecommendation任务上优于竞争方法。
Collaborative filtering is the most popular approach for recommender systems. One way to perform collaborative filtering is matrix factorization, which characterizes user preferences and item attributes using latent vectors. These latent vectors are good at capturing global features of users and items but are not strong in capturing local relationships between users or between items. In this work, we propose a method to extract the relationships between items and embed them into the latent vectors of the factorization model. This combines two worlds: matrix factorization for collaborative filtering and item embedding, a similar concept to word embedding in language processing. Our experiments on three real-world datasets show that our proposed method outperforms competing methods on top-nrecommendation tasks.