融合异构信息的低秩分解推荐模型研究

项目名称： 融合异构信息的低秩分解推荐模型研究

项目编号： No.61300076

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 辛欣

作者单位： 北京理工大学

项目金额： 27万元

中文摘要： 针对传统个性化推荐技术在稀疏数据可适性、推荐结果可解释性上面临的挑战，设计有效融合信息特征的推荐模型已成为推荐系统领域研究的热点。而现有方法存在异构个体信息偏好冲突、异构社交关系信息偏好冲突、异构上下文信息融合复杂度过高等问题。为此，本课题深入研究并改进融合异构信息的低秩分解推荐模型。通过建立独立的异构主题空间，并予以个性化加权融合，准确描述个性化异构个体信息偏好；通过设计融合异构社交关系信息的数据生成过程和正则化项，以及建立基于图结构的关联度量化方法，准确描述个性化异构社交关系信息偏好；通过将异构空间潜在向量映射到同构空间并加以线性融合的方式，降低融合异构上下文信息的计算复杂度。最后将上述改进整合成统一模型，通过对异构信息的有效融合，更准确地适刻画稀疏数据；同时通过对个性化异构信息偏好的准确描述，更完整的解释推荐结果。本课题将有力改善信息过载时代的用户体验及Web服务的收益。

中文关键词： 推荐系统；异构信息；协同过滤；张量分解；主题分析

英文摘要： Aiming at the data sparsity and the un-interpretable challenges of traditional recommender systems, designing effective recommendation frameworks with the fusion of the enriched Web information, has become a promising research direction. Existing methods, however, suffer from the following three limitations. (1) conflicts on personalized biases of heterogeneous individual information; (2) conflicts on personalized biases of heterogeneous social information; and (3) time-consuming problem in fusing heterogeneous context information. In this project, we are going to investigate and improve the factorization-based recommendation approaches with the fusion of heterogeneous information. For the first limitation, a personalized weighted linear combination of latent feature vectors from heterogeneous latent spaces is proposed; for the second limitation, a generative process and a regularization function for fusing heterogeneous social information are designed, and graph-based algorithms are employed to quantify the relationship under homogeneous social network; and for the third limitation, the context latent feature vectors are linearly combined with personalized weights, after the linear transformations from heterogeneous latent spaces. Therefore, all the above limitations can be solved. At last, all the above three

英文关键词： Recommender Systems；Heterogeneous Information；Collaborative Filtering；Tensor Factorization；Topic Analysis