Document Representation and Query Expansion Models for Blog Recommendation


  • Jaime Arguello Carnegie Mellon University
  • Jonathan L. Elsas Carnegie Mellon University
  • Jamie Callan Carnegie Mellon University
  • Jaime G. Carbonell Carnegie Mellon University


We explore several different document representation models and two query expansion models for the task of recommending blogs to a user in response to a query. Blog relevance ranking differs from traditional document ranking in ad-hocinformation retrieval in several ways: (1) the unit of output (the blog) is composed of a collection of documents (the blog posts) rather than a single document, (2) the query represents an ongoing and typically multifaceted interest in the topic rather than a passing ad-hoc information need and (3) due to the propensity of spam, splogs, and tangential comments, the blogosphere is particularly challenging to use as a source for high-quality query expansion terms. We address these differences at the document representation level, by comparing retrieval models that view either the blog or its constituent posts as the atomic units of retrieval, and at the query expansion level, by making novel use of the links and anchor text in Wikipedia1 to expand a user's initial query. We develop two complementary models of blog retrieval that perform at comparable levels of precision and recall. We also show consistent and significant improvement across all models using our Wikipedia expansion strategy.




How to Cite

Arguello, J., Elsas, J. L., Callan, J., & Carbonell, J. (2021). Document Representation and Query Expansion Models for Blog Recommendation. Proceedings of the International AAAI Conference on Web and Social Media, 2(1), 10-18. Retrieved from