Azarias Reda, Yubin Park, Mitul Tiwari, Christian Posse, and Sam Shah

In the 21st International Conference on Information and Knowledge Management (CIKM 2012)


 

Abstract

Search plays an important role in online social networks as it provides an essential mechanism for discovering members and content on the network. Related search recommendation is one of several mechanisms used for improving members’ search experience in finding relevant results to their queries. This paper describes the design, implementation and deployment of Metaphor, the related search recommendation system on , a professional social networking site with over 160 million members worldwide. Metaphor builds on a number of signals and filters that capture several dimensions of relatedness across member search activity. The system, which has been in live operation for over a year, has gone through multiple iterations and evaluation cycles. This paper makes three contributions. First, we provide a discussion of a large-scale related search recommendation system. Second, we describe a mechanism for effectively combining several signals in building a unified dataset for related search recommendations. Third, we introduce a query length model for capturing bias in recommendation click behavior. We also discuss some of the practical concerns in deploying related search recommendations.

BiBTeX

@article{redametaphor, title={Metaphor: a system for related search recommendations}, author={Reda, A. and Park, Y. and Tiwari, M. and Posse, C. and Shah, S.} }