Web Item Reviewing Made Easy By Leveraging Available User Feedback
This work addresses the challenge of time-consuming review writing for users on online platforms, but it is incremental as it builds on existing user feedback and optimization methods.
The paper tackles the problem of generating top-k meaningful phrases or tags to help users write detailed reviews for items by proposing a general-constrained optimization framework based on relevance, coverage, and polarity measures, and validates its effectiveness through experiments on synthetic and real web data.
The widespread use of online review sites over the past decade has motivated businesses of all types to possess an expansive arsenal of user feedback to mark their reputation. Though a significant proportion of purchasing decisions are driven by average rating, detailed reviews are critical for activities like buying expensive digital SLR camera. Since writing a detailed review for an item is usually time-consuming, the number of reviews available in the Web is far 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 propose general-constrained optimization framework based on three measures - relevance (how well the result set of tags describes an item), coverage (how well the result set of tags covers the different aspects of an item), and polarity (how well sentiment is attached to the result set of tags). By adopting different definitions of coverage, we identify two concrete problem instances that enable a wide range of real-world scenarios. We develop practical algorithms with theoretical bounds to solve these problems efficiently. We conduct experiments on synthetic and real data crawled from the web to validate the effectiveness of our solutions.