Multi-user Diverse Recommendations through Greedy Vertex-Angle Maximization

  • Pedro Dias
  • Joao Magalhaes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8819)


This paper presents an algorithm capable of providing meaningful and diversified product recommendations to small sets of users. The proposed approach works on a high-dimensional space of latent factors discovered by the bias-SVD matrix factorization techniques. While latent factor models have been widely used for single users, in this paper we formalize recommendations for multi-user as a multi-objective minimization problem. In the pursuit of recommendation diversity, we introduce a metric that explores the angles among product factor vectors and extracts from these a measurable real-life meaning in terms of diversity. In contrast to the majority of recommender systems for groups described in literature, our system employs a collaborative filtering approach based on latent factor space instead of content-based or ratings merging approaches.


Singular Value Decomposition Latent Factor Recommender System User Preference User Satisfaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pedro Dias
    • 1
  • Joao Magalhaes
    • 1
  1. 1.Dep. Computer ScienceUniversidade Nova de LisboaPortugal

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