LinkedIn is the world's largest professional network, with over 300 million members. One of the primary activities on the site is people search, for which LinkedIn members are both the users and the corpus. This paper presents insights about people search behavior on LinkedIn, based on a log analysis and a user study. In particular, it examines the role that network distance plays in name searches and non-name searches. For name searches, users primarily click on only one of the results, and closer network distance leads to higher click-through rates. In contrast, for non-name searches, users are more likely to click on multiple results that are not in their existing connections, but with whom they have shared connections. The results show that, while network distance contributes significantly to LinkedIn search engagement in general, its role varies dramatically depending on the type of search query.

Recommender systems provide users with products or content intended to satisfy their information needs. The primary evaluation measures for recommender systems emphasize either the perceived relevance of the recommendations or the actions driven by those recommendations (e.g., purchases on ecommerce sites or clicks on news and social networking sites). Unfortunately, this transactional emphasis neglects the inherently interactive nature of the user experience.

Research in information retrieval has focused on presenting the most relevant results to a user in response to a free-text search query. Research in database systems assumes a model where the user enters a formal query, and the results are exactly those the user requested. Neither community has emphasized user interaction—a critical concern for practical information access.

Endeca's approach to processing search queries in a distributed computing environment is predicated on concerns of correctness, scalability, and flexibility in deployment. Using a master-slave architecture, we are able to support classic search as well as more advanced features. We avoid bottlenecking the master with excessive computation or communication by limiting the information from the slaves in the expected case.