Recommendation for Multi-stakeholders and through Neural Review Mining

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 in MSRS. 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.