Recommendation systems are gaining increasing popularity in many application areas like
e-commerce, movie and music recommendations, tourism, news, advertisement, stock …

Exploiting temporal context has been proved to be an effective approach to improve
recommendation performance, as shown, eg in the Netflix Prize competition. Time-aware …

Contexts and social network information have been proven to be valuable information for
building accurate recommender system. However, to the best of our knowledge, no existing …

Albeit the implicit feedback based recommendation problem—when only the user history is
available but there are no ratings—is the most typical setting in real-world applications, it is …

Mashups are a dominant approach for building data-centric applications, especially mobile
applications, in recent years. Since mashups are predominantly based on public data …

Incorporating social network information and contexts to improve recommendation
performance has been drawing considerable attention recently. However, a majority of …

Recommender systems have recently been singled out as a fascinating area of research,
owing to the technological progress in mobile devices, such as smartphones and tablets, as …

Context-aware recommendation algorithms focus on refining recommendations by
considering additional information, available to the system. This topic has gained a lot of …

User beliefs about algorithmic systems are constantly co-produced through user interaction
and the complex socio-technical systems that generate recommendations. Identifying these …

Abstract Collaborative Filtering Recommender Systems predict user preferences for online
information, products or services by learning from past user-item relationships. A …