by Laurie Sullivan, (January 30, 2015)
Search engines process billions of queries monthly. They are great at checking facts, returning information about nearby restaurants or movie theaters, and finding product information on retail Web sites, but they’re not yet good at processing complex tasks that go beyond simple keyword queries often thought of as information exploration and discovery.
A new search engine developed at Helsinki Institute for Information Technology (HIIT) focuses on interactive intent modeling, an approach the researchers believe predicts the user’s search intent. It analyzes the interaction between humans and IR systems to enable information discovery that goes beyond search. It combines human computer interaction and machine learning.
The search engine, SciNet, changes searches into recognition tasks by showing keywords related to the user’s search in topic radar, Professor Giulio Jacucci said in a statement. In addition to Jacucci, researchers Tuukka Ruotsalo, Petri Myllymäki, and Samuel Kaski created interactive intent modeling.
Estimated intents are shown on what the researchers call an IntentRadar. The search is directed by targeting keywords and moving them around the screen. The position of the words on the screen can change the intent, which changes the results. The system refreshes the IntentRadar and search results each time the words move. The technology combines visualizing the search intent and direction, and interactive adaptation of the intent model, balancing exploration of the information space and exploitation of user feedback.
Google, Bing and Yahoo offer this sort of search exploratory search, but it requires searchers to visit the pages served up in the results — an act that lengthens the search process. Researchers tested the process with 20 participants, comparing the system to a traditional query-based engine to determine if the new search engine significantly improves the effectiveness of retrieval by providing access to more relevant information without having to spend more time acquiring the information.
The search engine could interface with wearable devices. Research explain that “IR systems can be extended by augmenting a real scene with predictions of what the user might find useful, shown as augmented reality on head-mounted displays (HMDs).” Someone’s implicit and explicit reactions to visualized content can reveal their intent and help improve the intent model in the person’s wearable device.
It turns out that “augmenting a user’s environment when visiting a poster session at a conference with visual cues and information can help the system collect information about the user’s intent even when the user is not actively engaged with a search engine.”
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