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The widespread use of online review sites over the past decade has motivatedbusinesses of all types to possess an expansive arsenal of user feedback tomark their reputation. Though a significant proportion of purchasing decisionsare driven by average rating, detailed reviews are critical for activities likebuying expensive digital SLR camera. Since writing a detailed review for anitem is usually time-consuming, the number of reviews available in the Web isfar from many. Given a user and an item our goal is to identify the top-$k$meaningful phrases/tags to help her review the item easily. We proposegeneral-constrained optimization framework based on three measures - relevance(how well the result set of tags describes an item), coverage (how well theresult set of tags covers the different aspects of an item), and polarity (howwell sentiment is attached to the result set of tags). By adopting differentdefinitions of coverage, we identify two concrete problem instances that enablea wide range of real-world scenarios. We develop practical algorithms withtheoretical bounds to solve these problems efficiently. We conduct experimentson synthetic and real data crawled from the web to validate the effectivenessof our solutions.

Web Item Reviewing Made Easy By Leveraging Available User Feedback