Reducing echo chamber effects: an allostatic regulator for recommendation algorithms

Objective:
Recommendation systems are prevalent on the Internet but are prone to feedback loops that cause “echo chamber” effects. These effects can have negative consequences for users’ well-being, diversity of information, and social cohesion. Therefore, there is a need for novel techniques to combat echo chamber effects and promote healthier online experiences.

Method:
We present an allostatic regulator for recommendation systems based on opponent process theory. This regulator can be applied to the output layer of any existing recommendation algorithm to dynamically restrict the proportion of potentially harmful or polarised content recommended to users, based on the users’ recent content history. We implement our prototype algorithm as a code wrapper for a supervised K-Nearest Neighbors algorithm for movie recommendations and evaluate its performance using simulated user data.

Results:
Our results show that allostatic regulation is effective at reducing echo chamber effects in a simulated population. The method can be used for regulating the entire range of possible online content and can adapt to evolving user behaviours.

Conclusions:
The allostatic regulator is a promising technique for mitigating echo chamber effects, providing app developers with a flexible tool to help users self-regulate their online experiences.