Collaborative filtering (CF) is a common recommendation approach that relies on user-item
ratings. However, the natural sparsity of user-item rating data can be problematic in many …

We describe a novel, multi-task recommendation model, which jointly learns to perform
rating prediction and recommendation explanation by combining matrix factorization, for …

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 …

This paper describes an approach for generating rich and compelling explanations in
recommender systems, based on opinions mined from user-generated reviews. The …

Explaining recommendations helps users to make better decisions. We describe a novel
approach to explanation for recommender systems, one that drives the recommendation …

User reviews have become an important source for recommending and explaining products
or services. Particularly, providing explanations based on user reviews may improve users' …

In this paper, we explore a large multimodal dataset of about 65k albums constructed from a
combination of Amazon customer reviews, MusicBrainz metadata and AcousticBrainz audio …

Providing explanations based on user reviews in recommender systems (RS) may increase
users' perception of transparency or effectiveness. However, little is known about how these …

Explaining system-generated recommendations based on user reviews can foster users'
understanding and assessment of the recommended items and the recommender system …

Traditionally, recommender systems have relied on user preference data (such as ratings)
and product descriptions (such as meta-data) as primary sources of recommendation …