Recommendation for Multi-Stakeholders and through Neural Review Mining

Abstract

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.

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.