We investigate the impact of collaborative filtering recommender algorithms (eg, Amazon's
“Customers who bought this item also bought”) commonly used in e-commerce on sales …

Personalization is becoming ubiquitous on the World Wide Web. Such systems use
statistical techniques to infer a customer's preferences and recommend content best suited …

In this paper, we study collaborative filtering (CF) in an interactive setting, in which a
recommender system continuously recommends items to individual users and receives …

We present results from a large-scale, randomized field experiment on Spotify testing the
effect of personalized recommendations on consumption diversity. In the experiment, both …

Existing recommender systems mostly focus on recommending individual items which users
may be interested in. User-generated item lists on the other hand have become a popular …

Recommender systems are becoming integral to how consumers discover media. The value
that recommenders offer is personalization. In environments with many product choices …

We study product recommendations in an online environment where a firm provides
strategic product recommendations to consumers. We develop an analytical framework to …

This paper describes the main characteristics of GRSK, a Generalist Recommender System
Kernel. It is a RS based on the semantic description of the domain, which allows the system …

Personalized recommendation has been proved effective as a content discovery tool for
many online news publishers. As fresh news articles are frequently coming to the system …

Introducing consumed items as users' implicit feedback in matrix factorization (MF) method,
SVD++ is one of the most effective collaborative filtering methods for personalized …