The authors identified query awareness as a very important feature in collaborative searching, which allows group members to not only share their query terms, but also learn better query formulation techniques from one another. Another component of SearchTogether that facilitates awareness is the display of page-specific metadata. This region includes several pieces of information about the displayed page, including group members who viewed the given page, and their comments and ratings. The authors claim that such visitation information can help one either choose to avoid a page already visited by someone in the group to reduce the duplication of efforts, or perhaps choose to visit such pages, as they provide a sign of promising leads as indicated by the presence of comments and/or ratings. "Division of labor" in SearchTogether is implemented in three ways: (1) “Split Search” allows one to split the search results among all online group members in a round-robin fashion, (2) “Multi-Engine Search” takes a query and runs it on n different search engines, where "n" is the number of online group members, (3) manual division of labor can be facilitated using integrated IM. Finally, the "persistence" feature in SearchTogether is instantiated by storing all the objects and actions, including IM conversations, query histories, recommendation queues, and page-specific metadata. Such data about all the group members are available to each member when he/she logs in. This allows one to easily carry a multi-session collaborative project. Cerchiamo is a collaborative information seeking tool that explores issues related to algorithmic mediation of information seeking activities and how collaborators' roles can be used to structure the user interface. Cerchiamo introduced the notion of algorithmic mediation, that is, the ability of the system to collect input asynchronously from multiple collaborating searchers, and to use these multiple streams of input to affect the information that is being retrieved and displayed to the searchers. Cerchiamo collected judgments of relevance from multiple collaborating searchers and used those judgments to create a ranked list of items that were potentially relevant to the information need. This algorithm prioritized items that were retrieved by multiple queries and that were retrieved by queries that also retrieved many other relevant documents. This rank fusion is just one way in which a search system that manages activities of multiple collaborating searchers can combine their inputs to generate results that are better than those produced by individuals working independently. Cerchiamo implemented two roles—Prospector and Miner—that searchers could assume.