Quantifying the Effects of Recommendation Systems
Recommendation systems today exert a strong influence on consumer behavior and individual perceptions of the world. By using collaborative filtering (CF) methods to create recommendations, it generates a continuous feedback loop in which user behavior becomes magnified in the algorithmic system. Popular items get recommended more frequently, creating the bias that affects and alters user preferences. In order to visualize and compare the different biases, we will analyze the effects of recommendation systems and quantify the inequalities resulting from them.
Code (0)
등록된 구현이 없습니다.
Tasks
Collaborative FilteringRecommendation SystemsSimilar Papers 제목 키워드 기반
Metrics for popularity bias in dynamic recommender systems
Albeit the widespread application of recommender systems (RecSys) in our daily lives, rather limited research has been done on quantifying unfairness and biases present in such systems. Prior work largely focuses on dete…
Collaborative FilteringRecommendation SystemsDetecting and Quantifying Malicious Activity with Simulation-based Inference
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, …
Probabilistic ProgrammingUser IdentificationAlgorithmic Drift: A Simulation Framework to Study the Effects of Recommender Systems on User Preferences
Digital platforms such as social media and e-commerce websites adopt Recommender Systems to provide value to the user. However, the social consequences deriving from their adoption are still unclear. Many scholars argue …
Recommendation SystemsQuantifying Availability and Discovery in Recommender Systems via Stochastic Reachability
In this work, we consider how preference models in interactive recommendation systems determine the availability of content and users' opportunities for discovery. We propose an evaluation procedure based on stochastic r…
Interactive RecommendationRecommendation SystemsConf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods
Recommender systems based on graph neural networks perform well in tasks such as rating and ranking. However, in real-world recommendation scenarios, noise such as user misuse and malicious advertisement gradually accumu…
PredictionRecommendation Systems