Abstract: A method is provided for reporting and analyzing user search behaviors in a large scale heterogeneous search engine platform. Content repository managers want to understand how users search for content in their repository including what categories and attributes users are interested in, how users were referred to the site, and which searchable items were viewed. The method provides a low-cost alternative to OLAP and data warehouse solutions and exploits the scalability and user interface of a search engine. Furthermore, the taxonomy of the content repository needed for analysis is already known to the search engine, and need not be exported or represented in a different format required by another tool. Data analysis can be conducted interactively and in real-time.

Abstract: A method is provided for responding to user search requests with suggested categories and attributes that have a high probability of being useful to the user for refining the search. The method is described in the context of a shared search engine platform in which multiple vertical domain repositories reside. The common search engine can search all of the repositories in a single search. The multiple vertical domain repositories can be heterogeneous in type, size, and semantics. Choosing search hints in the face of such diversity of content can be a challenge. The approach uses a “relative density” measure to determine which categories and attributes to recommend and overcomes the problem of repositories with more content dominating the chosen search terms that are returned to the user.