Abstract
In this work, we want to show that the introduction of categories can strongly improve the performance of recommendation, within the new digitally infrastructured societies. We state that, inside these highly dynamic contexts, in which more and more people are connected to each other but a substantial part of the communication happens between strangers, it is fundamental to restructure the concept of recommendation. We strongly believe that a good solution for many situations would be to combine inferential processes with recommendations, i.e. focusing on recommending categories of agents rather than specific individuals. Specifically, in this work we prove that category’s recommendations are more robust to untrustworthy recommenders than individual recommendation. We tested our idea by the mean of a multi-agent social simulation. The results we obtained are in agreement with our hypotheses and can be of important interest for the development of this sector.
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Notes
- 1.
This aspect may become relevant in a very wide network, because of the latency time.
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Acknowledgments
This work is partially supported by the project CLARA—CLoud plAtform and smart underground imaging for natural Risk Assessment, funded by the Italian Ministry of Education, University and Research (MIUR-PON).
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Falcone, R., Sapienza, A. (2019). Selecting Trustworthy Partners by the Means of Untrustworthy Recommenders in Digitally Empowered Societies. In: Demazeau, Y., Matson, E., Corchado, J., De la Prieta, F. (eds) Advances in Practical Applications of Survivable Agents and Multi-Agent Systems: The PAAMS Collection. PAAMS 2019. Lecture Notes in Computer Science(), vol 11523. Springer, Cham. https://doi.org/10.1007/978-3-030-24209-1_5
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