The Public Service Approach to Recommender Systems : Filtering to Cultivate

Online media consumption has been radically transformed by how media companies algorithmically recommend content to their users. Public service media (PSM) have also realized the potential of recommender systems and are increasingly using these technologies to personalize their online offering. PSM are on the other hand required to disseminate diverse content, which can be incompatible with the logics of commercial recommender systems that primarily seek to drive up media consumption. Drawing on previous research on selective exposure and media diversity, this study presents the results from interviews with ten PSM informants across Europe, revealing that data scientists within these organizations are highly aware of the effects recommendations have on media consumption, and design the PSM online services accordingly. This study contributes with in-depth knowledge of how diversity has been interpreted at operational levels in PSM and how recommender systems are being adapted to a non-commercial setting.Peer reviewe

Explicação de Recomendações com Diversificação: uma Revisão Bibliográfica

Esse artigo a apresenta o resultado de uma revisão bibliográfica  sobre explicação de recomendação com diversificação. Constatou-se com  base nela que nenhuma pesquisa propôs ainda estudar como gerar explicação de recomendação com diversificação. Foram encontrados apenas trabalhos que indicam a necessidade de haver explicação de recomendação com diversificação. A partir dessa constatação de necessidade de pesquisa propõe-se, como trabalho futuro, investigar e desenvolver uma abordagem de  explicação de recomendação com diversificação. Essa abordagem terá que  gerar explicações que sejam interpretáveis e persuasivas de modelos  complexos de recomendação baseados em algoritmos de aprendizagem de máquina. Para avaliação experimental da abordagem de explicação de recomendação há possibilidade, dentro do grupo de pesquisa de Sistemas de  Informação da UFRGS, de aplicar e avaliar essa abordagem de explicação no domínio de Cidades Inteligentes

Making Sense of Responsibility: A Semio-Ethic Perspective on TikTok’s Algorithmic Pluralism

Utilizing the data collected from 40 in-depth interviews, this study explores: How do users perceive social media platforms’ responsibility in designing algorithms? What do users perceive as diverse or similar in the content generated by algorithmic recommendation systems? The analysis discusses and evaluates the tension between (a) how the platform’s algorithm feeds users similar videos that they highly appreciate and, inversely, (b) how the recommendation of similar videos might limit the diversity of content to which the user is exposed. The analysis adopts a semio-ethic framework to understand why algorithmic platforms like TikTok are perceived to be so efficient in promoting an apparent perception of inclusivity while deliberately erasing alterity and promoting universal sameness. Although videos recommended by TikTok might appear to satisfy computational criteria of diversity, the outcome masks the absence of algorithmic pluralism. The algorithm generates socially desirable videos to allow users to feel comfortable in their in-group. In other words, recommended videos perpetuate a digital form of conformism in a conscious attempt to create the illusion of a more plural community. Advancing the study of algorithmic pluralism is therefore crucial to evaluate the extent to which plurality is understood by users, and what assumptions and ethics underpin the cultures that foster algorithmic recommendation design

Understanding the role of latent feature diversification on choice difficulty and satisfaction

People like variety and often prefer to choose from large item sets. However, large sets can cause a phenomenon called “choice overload”: they are more difficult to choose from, and as a result decision makers are less satisfied with their choices. It has been argued that choice overload occurs because large sets contain more similar items. To overcome this effect, the present paper proposes that increasing the diversity of item sets might make them more attractive and satisfactory, without making them much more difficult to choose from. To this purpose, by using structural equation model methodology, we study diversification based on the latent features of a matrix factorization recommender model. Study 1 diversifies a set of recommended items while controlling for the overall quality of the set, and tests it in two online user experiments with a movie recommender system. Study 1a tests the effectiveness of the latent feature diversification, and shows that diversification increases the perceived diversity and attractiveness of the item set, while at the same time reducing the perceived difficulty of choosing from the set. Study 1b subsequently shows that diversification can increase users’ satisfaction with the chosen option, especially when they are choosing from small, diverse item sets. Study 2 extends these results by testing our diversification algorithm against traditional Top-N recommendations, and finds that diverse, small item sets are just as satisfying and less effortful to choose from than Top-N recommendations. Our results suggest that, at least for the movie domain, diverse small sets may be the best thing one could offer a user of a recommender system

Understanding the role of latent feature diversification on choice difficulty and satisfaction

\u3cp\u3ePeople like variety and often prefer to choose from large item sets. However, large sets can cause a phenomenon called “choice overload”: they are more difficult to choose from, and as a result decision makers are less satisfied with their choices. It has been argued that choice overload occurs because large sets contain more similar items. To overcome this effect, the present paper proposes that increasing the diversity of item sets might make them more attractive and satisfactory, without making them much more difficult to choose from. To this purpose, by using structural equation model methodology, we study diversification based on the latent features of a matrix factorization recommender model. Study 1 diversifies a set of recommended items while controlling for the overall quality of the set, and tests it in two online user experiments with a movie recommender system. Study 1a tests the effectiveness of the latent feature diversification, and shows that diversification increases the perceived diversity and attractiveness of the item set, while at the same time reducing the perceived difficulty of choosing from the set. Study 1b subsequently shows that diversification can increase users’ satisfaction with the chosen option, especially when they are choosing from small, diverse item sets. Study 2 extends these results by testing our diversification algorithm against traditional Top-N recommendations, and finds that diverse, small item sets are just as satisfying and less effortful to choose from than Top-N recommendations. Our results suggest that, at least for the movie domain, diverse small sets may be the best thing one could offer a user of a recommender system.\u3c/p\u3