Leveraging tagging for neighborhood-aware probabilistic matrix factorization
Leveraging tagging for neighborhood-aware probabilistic matrix factorization
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DOI：
10.1145/2396761.2398531
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Le Wu;Enhong Chen;Qi Liu;Linli Xu;Tengfei Bao;Lei Zhang
中科院分区：
文献类型：
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作者：
Le Wu;Enhong Chen;Qi Liu;Linli Xu;Tengfei Bao;Lei Zhang
协同过滤（CF）是一种流行的方法来建立推荐系统，并已成功地应用于许多应用。一般而言，CF的两种方法，局部邻域方法和整体矩阵分解模型，得到了广泛的研究。虽然一些先前的研究目标是结合这两种方法的互补优势，性能仍然是有限的，由于极端稀疏的评级数据。因此，有必要考虑更多的信息，以更好地反映用户的偏好和项目内容。为此，在本文中，通过利用额外的标记数据，我们提出了一种新的统一的两阶段推荐框架，命名为邻域感知概率矩阵分解（NHPMF）。具体地，我们首先使用标记数据来选择每个用户和每个项目的邻居，然后在矩阵分解中在每个用户（项目）的潜在特征向量上添加唯一的高斯分布，以确保相似的用户（项目）将具有相似的潜在特征。由于所提出的方法可以有效地探索外部数据源（即，标签数据）在统一的概率模型中，它会导致更准确的推荐。在两个真实的世界数据集上的大量实验结果表明，我们的NHPMF模型优于最先进的方法。
Collaborative Filtering(CF) is a popular way to build recommender systems and has been successfully employed in many applications. Generally, two kinds of approaches to CF, the local neighborhood methods and the global matrix factorization models, have been widely studied. Though some previous researches target on combining the complementary advantages of both approaches, the performance is still limited due to the extreme sparsity of the rating data. Therefore, it is necessary to consider more information for better reflecting user preference and item content. To that end, in this paper, by leveraging the extra tagging data, we propose a novel unified two-stage recommendation framework, named Neighborhood-aware Probabilistic Matrix Factorization(NHPMF). Specifically, we first use the tagging data to select neighbors of each user and each item, then add unique Gaussian distributions on each user's(item's) latent feature vector in the matrix factorization to ensure similar users(items) will have similar latent features}. Since the proposed method can effectively explores the external data source(i.e., tagging data) in a unified probabilistic model, it leads to more accurate recommendations. Extensive experimental results on two real world datasets demonstrate that our NHPMF model outperforms the state-of-the-art methods.