Recommender Systems : A Subspace Clustering Approach
Recommender Systems : A Subspace Clustering Approach
复制标题
推荐系统：子空间聚类方法
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Lance Parsons
中科院分区：
文献类型：
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作者：
Nitin Agarwal;Ehtesham Haque;Huan Liu;Lance Parsons
来自同一实验室的研究人员经常花费大量时间搜索与其当前项目相关的已发表文章。尽管有着相似的兴趣，他们仍进行独立且耗时的搜索。虽然他们可以在事后共享结果，但他们无法在搜索过程中利用以前的搜索结果。我们提出了一种研究论文推荐系统，该系统通过根据实验室其他人之前执行的搜索提供推荐来增强现有搜索引擎，从而避免此类耗时的搜索。大多数现有的推荐系统都是为拥有数百万用户的商业领域开发的。与大量的在线研究论文相比，研究论文领域的用户相对较少。此类数据的两个主要挑战是维度多和数据稀疏。该论文的新颖贡献是解决这些问题的可扩展子空间聚类算法（SCuBA）。综合数据集和基准数据集都用于评估聚类算法，并证明它在推荐研究论文时比传统的协同过滤方法表现更好。
Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBA) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers.