Internet search is an integral part of our daily lives. Everyday tasks such as conducting scholarly research, shopping, interacting with friends, choosing a university, or finding a nearby Thai restaurant have dramatically changed through the advent of online search. But as our search needs change and grow, search must also evolve.
Fortunately, search is a perpetual work in progress. Search engines such as Google and Bing! continuously strive to improve the search experience by making algorithmic changes and incorporating new features. Text matching, semantic, social, and vertical search are arguably some of the largest developments within the evolution of search and decision making.
Initially, search was restricted to text matching. A query for “apple cider” would comb the web for content including the words “apple” and “cider”. The results for such a query may be articles on apple cider, recipes for apple cider, or pages for a well known brand of apple cider. Although these results match the text entered by the user, these results do not help a user make a decision on which apple cider to purchase or which recipe to use to make apple cider.
Text matching search evolved into semantic search in order to better understand a user’s intent when searching. For example, in Google a query for NFL scores will list the latest scores of NFL games right on the search page rather than returning a list of pages that contain NFL scores. Despite seamlessly providing up-to-date information on NFL scores, Google’s semantic search won’t help a user decide which players to use for their fantasy football roster. Text matching and semantic search are highly effective for finding information but not making decisions.
In an attempt to help users make decisions through online search, search engines began to leverage the power of social media. Search engine Bing! integrated social signals into its search engine results in order to increase users’ decision making confidence. Much like people seek advice from friends and family as part of their decision making process in real life, social search was designed to integrate advice from friends, experts, and enthusiasts into search engine results. Just like search engine goliaths Bing and Google, Facebook also has plans for a social search engine of its own. Social search has definite potential but is somewhat of an echo chamber due to a lack of significant participation among search engine users. When it comes to the effectiveness of social search for online decision making, the jury is still out.
At this point in the evolution of search, vertical search is the best tool for online decision making–given that a user knows which particular vertical they want to make a decision in. Unlike text matching, semantic, or social search, vertical search is an entirely separate entity from general online search engines like Google or Bing!. Vertical search only delivers specialty content or data for specific categories or genres. Rather than combing the entire web for content that matches your search query, vertical search engines focus the decision making to a single topic such as cars, travel, or music. As it is now, search is too vast to be all things to all people. A vertical search engine provides the focus and refinement necessary for making decisions online.
Vertical search engines are a great way for category experts to take back a portion of the web. WebKite is hoping to fill that gap by giving users the tools to create their own vertical search engines and help their users make decisions.