多域协同过滤

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摘要翻译：
协同过滤是一种有效的推荐方法，它根据具有相似兴趣的其他用户的偏好来预测用户对某一项目的偏好。协同过滤方法的一大挑战是数据稀疏性问题，因为每个用户通常只对很少的项目进行评分，因此评分矩阵非常稀疏。在本文中，我们通过同时考虑不同领域中的多个协同过滤任务，并利用领域之间的关系来解决这个问题。我们将其称为多域协同过滤(MCF)问题。为了解决MCF问题，我们提出了一个概率框架，该框架使用概率矩阵分解对每个领域的评级问题进行建模，并通过自动学习领域之间的相关性来自适应地跨领域转移知识。我们还针对不同的领域引入了链接函数来纠正它们的偏差。在几个实际应用中进行了实验，并与一些有代表性的方法进行了比较，验证了本文方法的有效性。
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英文标题：
《Multi-Domain Collaborative Filtering》
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作者：
Yu Zhang, Bin Cao, Dit-Yan Yeung
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Collaborative filtering is an effective recommendation approach in which the preference of a user on an item is predicted based on the preferences of other users with similar interests. A big challenge in using collaborative filtering methods is the data sparsity problem which often arises because each user typically only rates very few items and hence the rating matrix is extremely sparse. In this paper, we address this problem by considering multiple collaborative filtering tasks in different domains simultaneously and exploiting the relationships between domains. We refer to it as a multi-domain collaborative filtering (MCF) problem. To solve the MCF problem, we propose a probabilistic framework which uses probabilistic matrix factorization to model the rating problem in each domain and allows the knowledge to be adaptively transferred across different domains by automatically learning the correlation between domains. We also introduce the link function for different domains to correct their biases. Experiments conducted on several real-world applications demonstrate the effectiveness of our methods when compared with some representative methods.
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PDF链接：
https://arxiv.org/pdf/1203.3535

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