|Image: Eric Ziegler|
How often do you hear someone say, "why doesn't our enterprise search work as well as Google search? Bing?" or "Why can't I find the content I want to find." or "I can't believe our search engine sucks." or "Our enterprise has a very small fraction of the content that Google searches and I still can't find the content I am looking for." or "FIX IT!".
In my previous blog posts, I started a series of posts about internal enterprise search and how it is not as good as internet search. In my first post, I provided an overview of how internal social interactions can improve internal search engine results. In my second post, I discussed how using the context of the employee can provide enterprise search engines a boost in providing improved search results. As a definition for what I meant by employee context I proposed that employee context is made up of a wide variety of types of information, including HR system information, social profile information, social connections and social interactions. In this blog post, I plan to discuss how the power of social connections can be used to improve search results within the enterprise.
When I mention connections, I am referring to the idea that one employee "follows" another employee. This following is similar to those external social networking sites such as facebook, twitter and google+. But how can these connections between employees be used to improve search results? Below are several illustrations of how connections can improve search results for each employee. For each of the examples below, I am using the following base example to illustrate my point:
Joe, an employee, follows five other employees and has 15 employees following him (Joe). In addition, the five employees Joe is following, follow a combined 20 more people (some with multiple people following the same person). Each of these connections (both direct and indirect) can play a key role in improving Joe's search results.
Directly following: Joe is directly following five other employees. Content created, modified or interacted with (comments, likes, tagging, bookmarking, etc.) by these people has a higher importance to Joe then other employees. Think about it, Joe is following these people for a reason. So why is the content these people create, modify and interact with not given higher relevance when providing search results to Joe?
Directly being followed: Similar reasoning can be used for the content created by the 15 people following Joe, but it goes in reverse. Joe does not realize that the 15 people are creating good content. Joe is not following any of these people, but they are following Joe because he creates content that is related to ideas each of them are interested in. Because of this, the content these people create has a higher chance of being valuable to Joe, he just doesn't know it. But there is a caveat to this, Joe is not following these people, either because he has knowingly not followed these people or he has not discovered these people. Because of this, the content created by these 15 employees should not receive as much of a relevance boost as content created by the direct followers.
Indirectly Following: Again, similar reasoning can be used for the indirect followers. Joe is following 5 people who are following a total of 20 people that Joe is not following. Since Joe trusts and follows those 5 people, there is some merit and a higher chance that the content created by the 20 indirect employees will be of higher relevance and importance to Joe. Because of this, the content created by these 20 employees should get a relevance boost in the search results. But just as the 15 employees that follow Joe, there is a caveat. Joe is not following these people, either because he has knowingly not followed these people or he has not discovered these people. Because of this the content created by these 20 employees should not receive as much of a relevance boost as content created by the direct followers.
With these improved relevance boosts for people direclty following, directing being followed and indirectly following, Joe searches on the term Java and receives results with an improved relevance boost for the 5 employees he is following, the 15 employees that are following him and the 20 indirect employees. In addition, the content from the 5 direct employees has the largest relevance boost, with the 15 employees following Joe and the 20 indirect employees he is following improving the relevance but not with as much of a boost in relevance.
With these search engine algorithm improvements, the search results have just gotten tremendously improved, making searching for content a better experience for Joe and every other employee. In future blog posts, I plan on continuing to discuss topics of how to improve internal enterprise search engine results. Future topics include reviewing how HR systems, employee profiles and social interactions can be used to improve search results. So check back periodically to hear my thoughts on how enterprise search can be much better than it is today.
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