The new search engine uses what Esteban Kozak, a senior product manager at , called a personalized relevance algorithm to pick out the most relevant users in the 31 million-member LinkedIn community.
In a , Kozak noted that the searcher's network is a key factor in ranking search results. Therefore, every matching search result is evaluated based on who is executing the search. "The end result is a personalized relevance algorithm that places the professionals that are most likely to be of interest at the top of the first search results page. We synthesized over a thousand pieces of feedback and analyzed data from over a billion search queries" in creating the engine, he said
New features include an "In Common" field, which lets users find what connections and groups he or she shares with the users listed in search results. In addition, users can customize their views of results. Thus, users can add or remove fields based on their own needs.
"We also saw in the data that many of you use search to get to your connections quickly," Kozak noted. "In order to make it more efficient, we developed a type-ahead widget that recommends connections as you type from any people search box."
Jason Kincaid, a blogger at , the company is looking for the revamped search engine to streamline the most often used features on the network by the of business users.