Multi-user Diverse Recommendations through Greedy Vertex-Angle Maximization

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
Publication statusPublished - 2014
EventIntelligent Data Analysis -
Duration: 1 Jan 2014 → …


ConferenceIntelligent Data Analysis
Period1/01/14 → …


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