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A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity

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Bias and Social Aspects in Search and Recommendation (BIAS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1245))

Abstract

Once we subscribe to an e-commerce portal, or to a social media website, we interact with multiple brands and with content from numerous providers. However, a unique user profile is created, containing all our preferences. Suppose that a company wants to understand who are its customers. It wants to treat costumers as a target, and understand what campaigns the company should run on them. On the one hand, an approach that clusters the users and performs group recommendations would be useful, while on the other hand, a generic user profile would not be helpful, since the preferences in it are not specific for a brand. Hence, we have to determine multiple user clusterings (one for each brand). This task makes the problem of producing group recommendation challenging, since little and very sparse information about the users is available, and for each pair of users we have to detect as many similarities as the brands existing in the system. To tackle this problem, in this paper, we introduce a novel and optimal measure to compute the similarity between users, based on Kolmogorov complexity. Further, we test it in the group recommendation scenario. The results show that our similarity measure can provide similar accuracy when compared to classical measures, but with significant performance gains, having a strictly lower time complexity than the state-of-the-art similarity measure.

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Notes

  1. 1.

    In order to speed up the process furthermore and embrace the concept of fast group recommendation proposed by Ntoutsi et al. [32], we also considered an alternative to the KNN approach, in which the neighbors were only selected inside the cluster of the target user. However, results show that, in our context, the effectiveness decreases. These results are not presented, to improve the readability of the paper.

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Acknowledgments

G. Ramos is with Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Portugal. This work was supported in part by FCT project POCI-01-0145-FEDER-031411-HARMONY. Further, this work was developed under the scope of R&D Unit 50008, financed by the applicable financial framework (FCT/MEC through national funds and when applicable co-funded by FEDER - PT2020 partnership agreement). The first author acknowledges the support of the DP-PMI and Fundação para a Ciência e a Tecnologia (Portugal), through scholarship SFRH/BD/52242/2013 and the support of Instituto de Telecomunicações through the research grant - BIM/No154 - 16/11/2017 - UID/EEA/50008/2017.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto, Porto, Portugal

    Guilherme Ramos

  2. SQIG - Instituto de Telecomunicações, Department of Mathematics, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal

    Guilherme Ramos & Carlos Caleiro

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  1. Guilherme Ramos
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  1. Eurecat - Centre Tecnològic de Catalunya, Barcelona, Spain

    Ludovico Boratto

  2. Unitelma Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  3. University of Cagliari, Cagliari, Italy

    Mirko Marras

  4. University of L’Aquila, L’Aquila, Italy

    Giovanni Stilo

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Ramos, G., Caleiro, C. (2020). A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Bias and Social Aspects in Search and Recommendation. BIAS 2020. Communications in Computer and Information Science, vol 1245. Springer, Cham. https://doi.org/10.1007/978-3-030-52485-2_10

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