Relational Collaborative Topic Regression for Recommender Systems
Relational Collaborative Topic Regression for Recommender Systems
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DOI：
10.1109/tkde.2014.2365789
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发表时间：
2015-05
影响因子：
8.9
通讯作者：
Hao Wang;Wu-Jun Li
中科院分区：
文献类型：
--
作者：
Hao Wang;Wu-Jun Li
协同过滤由于在推荐系统中的成功应用，已经成为数据挖掘和信息检索领域的研究热点。在传统的连续反馈方法中，只有反馈矩阵用于训练和预测，反馈矩阵包含用户对项目的显式反馈(也称为评级)或隐式反馈。通常，反馈矩阵是稀疏的，这意味着大多数用户与很少的项目交互。由于这种稀疏性问题，传统的仅有反馈信息的循环过滤算法性能不佳。近年来，许多研究人员提出利用项目内容(属性)等辅助信息来缓解内容抽取中的数据稀疏问题。协作主题回归(CTR)是其中的一种方法，它成功地整合了反馈信息和项目内容信息，取得了良好的效果。在许多实际应用中，除了反馈和项目内容信息外，项目之间还可能存在有助于推荐的关系(也称为网络)。本文提出了一种新的分层贝叶斯模型--关系协同主题回归(RCTR)，该模型通过将用户-项目反馈信息、项目内容信息和项目之间的网络结构无缝地集成到同一模型中来扩展关系协同主题回归。在真实数据集上的实验表明，该模型能够以更少的经验训练时间获得比最新方法更好的预测精度。此外，RCTR可以学习良好的可解释的潜在结构，这对推荐是有用的。
Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist relations (also known as networks) among the items which can be helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same model. Experiments on real-world datasets show that our model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation.