Finding My Needle in the Haystack: Effective Personalized Re-ranking of Search Results in Prospector

This paper provides an overview of Prospector, a personalized Internet meta-search engine, which utilizes a combination of ontological information, ratings-based models of user interests, and complementary theme-oriented group models to recommend (through re-ranking) search results obtained from an underlying search engine. Re-ranking brings "closer to the top" those items that are of particular interest to a user or have high relevance to a given theme. A user-based, real-world evaluation has shown that the system is effective in promoting results of interest, but lags behind Google in user acceptance, possibly due to the absence of features popularized by said search engine. Overall, users would consider employing a personalized search engine to perform searches with terms that require disambiguation and / or contextualization.