Towards novelty-driven recommender systems

Pietro Gravino; Bernardo Monechi; Vittorio Loreto

doi:10.1016/j.crhy.2019.05.014

From statistical physics to social sciences/De la physique statistique aux sciences sociales

Towards novelty-driven recommender systems
[Vers des systèmes de recommandation axés sur la nouveauté]

Pietro Gravino ¹ ; Bernardo Monechi ¹ ; Vittorio Loreto ^1,^2,³

¹ Sony Computer Science Laboratories, Paris, 6, rue Amyot, 75005 Paris, France
² Sapienza University of Rome, Physics Department, Piazzale Aldo Moro 2, 00185 Roma, Italy
³ Complexity Science Hub, Josefstädter Strasse 39, A-1080 Wien, Austria

Comptes Rendus. Physique, Volume 20 (2019) no. 4, pp. 371-379.

Résumé
Abstract

Nous recevons des recommandations à propos de tout et d'une manière omniprésente. Les systèmes de recommandation agissent comme des boussoles pour notre voyage dans des espaces conceptuels complexes, et nous comptons de plus en plus sur les recommandations pour fonder la plupart de nos décisions. Malgré leur efficacité et leur fiabilité extraordinaires, les systèmes de recommandation sont loin d'être parfaits. Ils présentent plutôt de sérieux inconvénients qui pourraient sérieusement réduire notre ouverture d'esprit et notre capacité à faire l'expérience de la diversité et à avoir des opinions contradictoires. Dans cet article, nous examinons attentivement les fondements mêmes des algorithmes de recommandation afin d'identifier les déterminants de ce que pourrait être la prochaine génération de systèmes de recommandation. Nous postulons qu'il est possible de surmonter les limites des systèmes de recommandation actuels en s'inspirant de la façon dont les gens recherchent les nouveautés et valorisent les nouvelles expériences. De ce point de vue, la notion de possible adjacent semble pertinente pour repenser les systèmes de recommandation d'une manière qui s'aligne mieux avec l'inclination naturelle des êtres humains vers des expériences nouvelles et agréables. Nous affirmons que cette nouvelle génération de prescripteurs pourrait aider à surmonter les écueils des technologies actuelles, à savoir la tendance au manque de diversité, à la polarisation, à l'émergence d'écho-chambres et à la désinformation.

We get recommendations about everything and in a pervasive way. Recommender systems act like compasses for our journey in complex conceptual spaces and we more and more rely on recommendations to ground most of our decisions. Despite their extraordinary efficiency and reliability, recommender systems are far from being flawless. They display instead serious drawbacks that might seriously reduce our open-mindedness and our capacity of experiencing diversity and possibly conflicting views. In this paper, we carefully investigate the very foundations of recommendation algorithms in order to identify the determinants of what could be the next generation of recommender systems. We postulate that it is possible to overcome the limitations of current recommender systems, by getting inspiration from the way in which people seek for novelties and give value to new experiences. From this perspective, the notion of adjacent possible seems a relevant one to redesign recommender systems in a way that better aligns with the natural inclination of human beings towards new and pleasant experiences. We claim that this new generation of recommenders could help in overcoming the pitfalls of current technologies, namely the tendency towards a lack of diversity, polarization, the emergence of echo-chambers and misinformation.

Publié le : 2019-05-01

DOI : 10.1016/j.crhy.2019.05.014

Keywords: Recommender systems, Novelties, Adjacent possible, Comfort zone
Mot clés : Systèmes de recommandation, Nouveautés, Adjacente possible, Zone de confort

Affiliations des auteurs :

Pietro Gravino ¹ ; Bernardo Monechi ¹ ; Vittorio Loreto ^{1, 2, 3}

¹ Sony Computer Science Laboratories, Paris, 6, rue Amyot, 75005 Paris, France
² Sapienza University of Rome, Physics Department, Piazzale Aldo Moro 2, 00185 Roma, Italy
³ Complexity Science Hub, Josefstädter Strasse 39, A-1080 Wien, Austria

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     title = {Towards novelty-driven recommender systems},
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     pages = {371--379},
     publisher = {Elsevier},
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     number = {4},
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     language = {en},
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Pietro Gravino; Bernardo Monechi; Vittorio Loreto. Towards novelty-driven recommender systems. Comptes Rendus. Physique, Volume 20 (2019) no. 4, pp. 371-379. doi : 10.1016/j.crhy.2019.05.014. https://comptes-rendus.academie-sciences.fr/physique/articles/10.1016/j.crhy.2019.05.014/

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