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Семинар ММИТ во вторник 17 октября: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews, Докладчик: Prof. Alexander Tuzhilin, Stern School of Business, NYU

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Во вторник, 17 октября 2017 года, состоится очередное заседание семинара "Математические модели информационных технологий" департамента анализа данных и искусственного интеллекта и МНУЛ "Интеллектуальные системы и структурный анализ" под руководством С.О. Кузнецова.

 

Место проведения: Кочновский проезд, 3. ауд. 32918:10

 

Title: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

 

Speaker: Prof. Alexander Tuzhilin, Leonard N. Stern Professor of Business and the Chair of the Department of Information, Operations and Management Sciences at the Stern School of Business, NYU

 

Abstract: This talk will present a new recommendation technique, called Sentiment Utility Logistic Model (SULM), that not only can recommend items of interest to the user, as traditional recommendation systems do, but also specific aspects of consumption of the items to further enhance user experiences with those items. For example, it can recommend the user to go to a particular restaurant (item) and also order some specific foods there, such as seafood (an aspect of consumption). SULM uses sentiment analysis of user reviews by first predicting the sentiment that the user may have about the item based on what she might express about the aspects of the item and then identifies the most valuable aspects of user’s potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant and what to order there. The proposed method was tested on three applications (restaurant, hotel, and beauty&spa), and it was experimentally shown that those users who followed the recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.

 

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