To Manipulate The Thinking Of Others Via Google
the loop between opinion formation and personalized recommendations
Ingrid Fadelli, Tech Xplore
at the University of Twente and CNRS have recently carried out a study
that explores the relationship between users' opinions and the
personalized recommendations they receive online. In their paper, which
was pre-published on arXiv, they proposed a model outlining this
interaction, then evaluated it through extensive simulations and a
all encounter recommender systems in our daily life, as soon as we reach
out on the Internet, whether browsing Facebook or Twitter or shopping on
Amazon," Paolo Frasca, one of the researchers who carried out the study,
told TechXplore. "These systems are tasked to select the information
that is most relevant to us."
recommender systems are designed to highlight particular online content
that matches the preferences of individual users browsing the internet.
In recent years, these systems have become increasingly popular, with
media platformsand other websites using them to
enhance user engagement, or to advertise products and services.
research conducted by Frasca and his colleagues was aimed at achieving a
better understanding of the interplay between users' opinions and the
personalised recommendations put forward by recommender systems. As
mathematicians, they developed a dynamicmodelof
the interconnection between user and recommended content.
recommender system is very simple, since it only has two items from
which to choose and it is characterised by a single parameter, which we
call epsilon," Frasca explained. "The system keeps record of how much
the items were appreciated (=clicked upon) in the past. At each time it
has to make arecommendation,
the systems tosses a (biased) coin that returns head with probability
epsilon (tail with probability 1-epsilon)."
the result of this coin tossing is head, the system recommends the most
successful item recorded in its history; if it shows tail, it recommends
an entirely random item. This process of randomisation allows the
researchers to choose 'epsilon' to ensure that the system effectively
balances diversity and accuracy in the recommendations it provides.
model represents the interaction between a single user and an online
news aggregator, in order to uncover the feedback loop between the
evolution of this user's opinion and the personalised recommendations.
It assumes that the user in question has a scalar opinion on a
particular issue, characterized by a binary position, and that this
opinion can be influenced by the news received online. Typically, the
user is thought to have a confirmation bias, meaning that she will have
a preference for content that confirms her opinion about a given issue.
researchers also assume that the recommender system's goal is that of
maximising the number of user clicks, and to attain it, it has to
compromise between exploring user preferences and exploiting them.
Extensive numerical simulations and amathematical
analysisof the model found that personalised
contents and confirmation bias both affected the evolution of a user's
opinions, with the extent of this effect being related to the
effectiveness of the recommender system.
have highlighted that the behaviours of user and recommender system feed
into each other in such a way that the behaviour of the user is
altered," Frasca said. "At the same time, the parameter epsilon provides
a knob to tune the amount of randomness and possibly mitigate the impact
research carried out by Frasca and his colleagues provided interesting
insight into the relationship between users' opinions and the
personalised recommendations they receive online. However, this insight
still needs to be validated further before it can be translated into
policy recommendations. The researchers are now working on improving
their model, to ensure that it better reflects real-life scenarios.
model is about one single user and two possible items," Frasca said.
"Clearly, in reality, both users and items are numerous. We plan to
extend the model to include a social network of users and a multiplicity
of items. In a sense, our recent work has been a stepping stone to a
more general model that is our next objective."