III: Small: RUI: Multi-dimensional Recommendation in Complex Heterogeneous Networks

通过推荐的个性化信息访问是当今在线信息系统的支柱。随着在线信息源变得更大、更多样和更复杂，需要新的推荐工具，在进行个性化推荐时可以集成所有数据维度。该项目将开发适合在线社交网络规模和复杂性的新推荐技术。特别是，它将研究如何使用网络的不同分解来创建多个推荐组件，以及如何将这些组件组合成单个加权集成。该项目将使个性化推荐的生成更加有效和高效，帮助用户即使在大型和复杂的信息网络中也能找到有用的连接。该项目将探索一种推荐方法，称为加权混合低重复性推荐（WHyLDR），该方法已被证明在社交网络推荐中是有效的。WHyLDR基于元路径展开将异构网络分解为二维矩阵的集合，其中元路径表示网络上链接关系的组合。使用学习的权重组合低维分量。该研究的一个挑战是元路径的数量是无限的。因此，该项目将探讨分析标准，以评估混合动力车各组成部分的相对价值。该项目将使用四个复杂的异构网络数据集进行实验，这些数据集来自非常不同的领域：就业，旅游，音乐和科学书目，使用各种推荐任务和评估指标。

Personalized information access via recommendation is a mainstay of today's online information systems. As online information sources become larger, more varied and more complex, new recommendation tools are needed that can integrate all of the dimensions of the data when making personalized recommendations. This project will develop new recommendation techniques appropriate to the scale and complexity of online social networks. In particular, it will study how different decompositions of a network can be used create multiple recommendation components, and how these components can be combined into a single weighted ensemble. The project will enable more effective and efficient generation of personalized recommendations, helping users find useful connections even in large and complex information networks.This project will explore a recommendation approach known as the Weighted Hybrid of Low-Dimensionality Recommenders (WHyLDR), which has proved effective in recommendation for the social web. WHyLDR decomposes heterogeneous networks into collections of two-dimensional matrices on the basis of meta-path expansions, where a meta-path represents a composition of link relations over the network. The low-dimensional components are combined using learned weights. One challenge of this research is that the number of meta-paths is unbounded. Therefore, the project will explore analytic criteria by which to evaluate the relative value of components in a hybrid. The project will experiment with four complex heterogeneous network data sets drawn from very different areas: employment, travel, music and scientific bibliography, using a variety of recommendation tasks and evaluation metrics.