Echo Chambers in Collaborative Filtering Based Recommendation Systems
Recommendation systems underpin the serving of nearly all online content in the modern age. From Youtube and Netflix recommendations, to Facebook feeds and Google searches, these systems are designed to filter content to the predicted preferences of users. Recently, these systems have faced growing criticism with respect to their impact on content diversity, social polarization, and the health of public discourse. In this work we simulate the recommendations given by collaborative filtering algorithms on users in the MovieLens data set. We find that prolonged exposure to system-generated recommendations substantially decreases content diversity, moving individual users into "echo-chambers" characterized by a narrow range of content. Furthermore, our work suggests that once these echo-chambers have been established, it is difficult for an individual user to break out by manipulating solely their own rating vector.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringDiversityRecommendation SystemsSimilar Papers 제목 키워드 기반
Dynamic Causal Collaborative Filtering
Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of rec…
Collaborative FilteringcounterfactualCounterfactual ReasoningRecommendation SystemsNetwork Flow Based Post Processing for Sales Diversity
Collaborative filtering is a broad and powerful framework for building recommendation systems that has seen widespread adoption. Over the past decade, the propensity of such systems for favoring popular products and thus…
Collaborative FilteringDiversityRecommendation SystemsA Survey on Echo Chambers on Social Media: Description, Detection and Mitigation
Echo chambers on social media are a significant problem that can elicit a number of negative consequences, most recently affecting the response to COVID-19. Echo chambers promote conspiracy theories about the virus and a…
MisinformationRecommendation SystemsUnderstanding Echo Chambers in E-commerce Recommender Systems
Personalized recommendation benefits users in accessing contents of interests effectively. Current research on recommender systems mostly focuses on matching users with proper items based on user interests. However, sign…
Recommendation SystemsImproving Recommendation System Serendipity Through Lexicase Selection
Recommender systems influence almost every aspect of our digital lives. Unfortunately, in striving to give us what we want, they end up restricting our open-mindedness. Current recommender systems promote echo chambers, …
DiversityRecommendation Systems