Abstract
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.
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Dias, P., Magalhaes, J. (2014). Multi-user Diverse Recommendations through Greedy Vertex-Angle Maximization. In: Blockeel, H., van Leeuwen, M., Vinciotti, V. (eds) Advances in Intelligent Data Analysis XIII. IDA 2014. Lecture Notes in Computer Science, vol 8819. Springer, Cham. https://doi.org/10.1007/978-3-319-12571-8_9
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DOI: https://doi.org/10.1007/978-3-319-12571-8_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12570-1
Online ISBN: 978-3-319-12571-8
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