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Gediminas Adomavicius
Researcher at University of Minnesota
Publications - 148
Citations - 20632
Gediminas Adomavicius is an academic researcher from University of Minnesota. The author has contributed to research in topics: Recommender system & Collaborative filtering. The author has an hindex of 43, co-authored 141 publications receiving 18796 citations. Previous affiliations of Gediminas Adomavicius include Facebook & New York University.
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Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Context-Aware Recommender Systems
TL;DR: An overview of the multifaceted notion of context is provided, several approaches for incorporating contextual information in recommendation process are discussed, and the usage of such approaches in several application areas where different types of contexts are exploited are illustrated.
Proceedings ArticleDOI
Context-aware recommender systems
TL;DR: This chapter argues that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations, and introduces three different algorithmic paradigms for incorporating contextual information into the recommendation process.
Journal ArticleDOI
Incorporating contextual information in recommender systems using a multidimensional approach
TL;DR: A multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the currentRecommender systems is presented.
Journal ArticleDOI
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
TL;DR: A number of item ranking techniques that can generate substantially more diverse recommendations across all users while maintaining comparable levels of recommendation accuracy are introduced and explored.