E-commerce recommender systems seek out matches between customers and items in
order to help customers discover more relevant and satisfying products and to increase the …

User-generated reviews are a plentiful source of user opinions and interests and can play
an important role in a range of artificial intelligence contexts, particularly when it comes to …

User-written product reviews contain rich information about user preferences for product
features and provide helpful explanations that are often used by shoppers to make their …

Recommendation systems make use of complex algorithms and methods to provide
recommendations to consumers. Typically, online rating schemes use a single rating metric …

Explanations are an important part of modern recommender systems. They help users to
make better decisions, improve the conversion rate of browsers into buyers, and lead to …

Traditionally, recommender systems operate based on user-behavior and rating data at the
personal and/or aggregate level. In this talk, I will try to go beyond this tradition to discuss …

In this paper we build on recent work on case-based product recommendation focused on
generating rich product descriptions for use in a recommendation context by mining user …

Previous research on Recommender Systems (RS), especially the continuously popular
approach of Collaborative Filtering (CF), has been mostly focusing on the information …

This paper describes a novel approach for generating explanations for recommender
systems based on opinions in user-generated reviews. We show how these opinions can be …

In the world of recommender systems, so-called content-based methods are an important
approach that rely on the availability of detailed product or item descriptions to drive the …