摘要翻译：
会议论文分配，即将论文提交给审稿人的任务，为推荐系统的研究提出了多方面的问题。除了预测“谁喜欢什么？”这一传统目标之外，会议管理系统还必须考虑到以下方面：审稿人的能力限制、对论文的适当审稿次数、专门知识建模、利益冲突以及兼顾审稿人偏好和会议目标的任务总体分配。其中，评价中的模型偏好和品味问题传统上是与审稿人分配的优化分开研究的。在本文中，我们提出了这两个方面的综合研究。首先，由于每个审稿人或每篇论文的数据很少（相对于其他推荐系统应用程序），我们展示了如何集成多个信息源来学习论文审稿人偏好模型。其次，我们的模型不仅在预测精度方面，而且在最终分配质量方面进行了评估。使用基于线性规划的分配优化公式，我们展示了我们的方法如何更好地探索未提供的分配空间，以最大化分配给审稿人的论文的整体亲和力。我们在IEEE ICDM 2007会议上展示了我们对真实审稿人偏好数据的结果。
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英文标题：
《Recommender Systems for the Conference Paper Assignment Problem》
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作者：
Don Conry, Yehuda Koren, Naren Ramakrishnan
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最新提交年份：
2009
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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：
  Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account aspects such as: reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Among these, issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of paper-reviewer assignment. In this paper, we present an integrated study of both these aspects. First, due to the paucity of data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn paper-reviewer preference models. Second, our models are evaluated not just in terms of prediction accuracy but in terms of the end-assignment quality. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer preference data from the IEEE ICDM 2007 conference.
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PDF链接：
https://arxiv.org/pdf/0906.4044