Community-based snippet-indexes for pseudo-anonymous personalization in web search

We describe and evaluate an approach to personalizing Web search that involves post-processing the results returned by some underlying search engine so that they re .ect the interests of a community of like-minded searchers.To do this we leverage the search experiences of the community by mining the title and snippet texts of results that have been selected by community members in response to their queries. Our approach seeks to build a community-based snippet index that re .ects the evolving interests of a group of searchers. This index is then sed to re-rank the results returned by the underlying search engine by boosting the ranking of key results that have been freq ently selected for similar q eries by community members in the past.