In the face of small, one or two word queries, 
high volumes of diverse documents on the Web are overwhelming 
search and ranking technologies that are based on
document similarity measures. The increase of multimedia data within 
documents sharply exacerbates the shortcomings of these approaches. 
Recently, research prototypes and commercial experiments 
have added techniques that augment similarity-based search and ranking. 
These techniques rely on judgments about the 'value' of documents. 
Judgments are obtained directly from users, 
are derived by conjecture based on observations of user behavior, 
or are surmised from analyses of documents and collections. 
All these systems have been pursued independently, 
and no common understanding of the underlying processes has been presented. 
We survey existing value-based approaches, develop a reference 
architecture that helps compare the approaches, 
and categorize the constituent algorithms. 
We explain the options for collecting value metadata, 
and for using that metadata to improve search, ranking of results, 
and the enhancement of information browsing. 
Based on our survey and analysis, we then point to several open problems.