Sihem Amer-Yahia is visiting Webdam from Tuesday 1 December to Thursday 31 December. She is a Senior Research Scientist at Yahoo! Research New-York. She will present her work on recommendation Friday 4 December at 3pm in the meeting room G008.
Title: I’ll Have What She’s Having: Recommendations on Social Content Sites
Abstract: We examine the challenges behind recommendations in social content sites. We use collaborative tagging sites (think del.icio.us, YouTube and Yahoo!Travel) as our application and report on our experiments in harvesting the collective tagging behavior to serve relevant content (think URLs, videos, travel destinations) to users. We address well-known and lesser-known problems in recommender systems such as over-specialization and data management for the masses. We conclude with open questions.
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Amelie Marian is visiting Webdam from Tuesday 1 September to Wednesday 30 September 2009 and from Tuesday 1 December to Thursday 31 December. She is an assistant professor at Rutgers University, New Jersey, USA. She will present her work on rating prediction using review texts Friday 4 December at 2pm in the meeting room G008.
Title: Beyond the Stars: Improving Rating Predictions using Review Text Content
Abstract: Online reviews are an important asset for users deciding to buy a product, see a movie, or go to a restaurant, as well as for businesses tracking user feedback. However, most reviews are written in a free-text format, and are therefore difficult for computer systems to understand, analyze, and aggregate. One consequence of this lack of structure is that searching text reviews is often frustrating for users; keyword searches typically do not provide good results as the same keywords routinely appear in good and in bad reviews. User experience would be greatly improved if the structure and sentiment information conveyed in the content of the reviews were taken into account. Our work focuses on identifying this structure and sentiment information from free-text reviews, and using this knowledge to improve user experience in accessing reviews. Specifically, we focused on improving recommendation accuracy in a restaurant review scenario.
We report on our classification effort, and on the insight on user-reviewing behavior that we gained in the process. We propose new ad-hoc and regression-based recommendation measures, that both take into account the textual component of user reviews. Our results show that using textual information results in better general or personalized restaurant score predictions than those derived from the numerical star ratings given by the users.
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