Abstract
With the increased generation and availability of big data in different application domains, there is a need for systems that “understand user’s search intents” and “serves data objects” in the background. Search intent is significant objects/topics represents, abstraction of user’s information needs, which a user often fails to, specify in information seeking. Thus, the fundamental search is shifting focus on “Find What You Need and Understand What You Find”. User intention predictions and personalized recommendations are two such methods, to formalize user’s intents. We investigated various information factors, which affect user’s search intentions and how these intents assist in an exploratory search. In particular, we proposed an algorithm for automatic intent modeling (AIM), based on user’s reviews and confidence assessments as search representatives of user’s search intents. In this process, we have revisited various related research efforts and highlighted inherent design issues.
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