FindiLike: A Preference Driven Entity Search Engine for Evaluating Entity Retrieval and Opinion Summarization

FindiLike: A Preference Driven Entity Search Engine for Evaluating Entity Retrieval and Opinion Summarization, Ganesan, Kavita A., and Zhai ChengXiang , (Best Demo), ACM Conference on Information and Knowledge Management (CIKM '13), San Francisco, (2013)


We describe a novel preference-driven search engine (FindiLike) which allows users to find entities of interest based on preferences and also allows users to digest opinions about the retrieved entities easily. FindiLike leverages large amounts of online reviews about various entities, and ranks entities based on how well their associated reviews match a user's preference query (expressed in keywords). FindiLike then uses abstractive summarization techniques to generate concise opinion summaries to enable users to digest the opinions about an entity. We discuss how the system can be extended to support in situ evaluation of two interesting new tasks, i.e., opinion-based entity ranking and abstractive summarization of opinions. The system is currently supporting hotel search and being extended to support in situ evaluation of these two tasks. We will demonstrate the system in the domain of hotel search and show how in situ evaluation can be supported through natural user interaction with the system.

This demo won the best demo award!

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