Tutorial at ACM Conference on Information & Knowledge Management (CIKM), Beijing, Nov 3-7, 2019
Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only
stakeholder in these traditional system. However, there could be multiple stakeholders
in several applications domains (e.g., e-commerce, movies, music). Recommendations
are necessary to be produced by balancing the needs of
different stakeholders. First session of this tutorial introduces
multi-stakeholder recommender systems (MSRS) with several
case studies, and discusses the corresponding methods and challenges
One of the current work in MSRS is the utility-based multi-stakeholder recommendation model which utilizes multi-criteria ratings to build the utility functions. However, the multi-criteria ratings or preferences may not be always available in any domains or applications. Review mining is usually used to extract user preferencesfrom texts or reviews. Potentially it could be used to infer the userpreferences on different aspects of the items. The second session of the tutorial will introduce and discuss neural review mining for recommendations.
Reviews in an e-commerce platform may be mined to address cold-start problem and to generate explanations. Our earlier tutorial covered aspect-based sentiment analysis of products and topic models/distributed representations that bridge vocabulary gap between user reviews and product descriptions. Focus in the second session of this tutorial instead is on recent neural methods for review text mining - covering hands-on code for its use to enhance product recommendation. Each section will introduce topics from various mechanism (e.g., attention) and task (e.g., review ranking) perspectives, present cutting-edge research and a walk-through of programs executed on Jupyter notebook using real-world data sets.
Time: Nov 3rd, 1:30 PM to 5:00 PM with coffee break (3:00 - 3:30 PM)
Location: China National Convention Center, Room 302A
Estimated Time Slots
Illinois Institute of Technology, USA
IIT Kharagpur, India