Current conversational recommender systems are unable to offer guarantees on the quality of their recommendations due to a lack of principled user utility models. We develop an ap...
Abstract. Traditional websites have long relied on users revealing their preferences explicitly through direct manipulation interfaces. However recent recommender systems have gone...
Collaborative filtering (CF) techniques have proved to be a powerful and popular component of modern recommender systems. Common approaches such as user-based and item-based metho...
Rachael Rafter, Michael P. O'Mahony, Neil J. Hurle...
—Due to the exponential growth of information on the Web, Recommender Systems have been developed to generate suggestions to help users overcome information overload and sift thr...
Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navig...
Abstract. Since more and more Web sites, especially sites of retailers, offer automatic recommendation services using Web usage mining, evaluation of recommender algorithms has bec...
Recommender systems are an emerging technology that helps consumers find interesting products and useful resources. A recommender system makes personalized product suggestions by e...
The collaborative filtering approach to recommender systems predicts user preferences for products or services by learning past useritem relationships. In this work, we propose no...
Recommender systems have emerged in the past several years as an effective way to help people cope with the problem of information overload. One application in which they have bec...
In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendat...