Eye Tracking as a Source of Implicit Feedback in Recommender Systems: A Preliminary Analysis

Eye tracking in recommender systems can provide an additional source of implicit feedback, while helping to evaluate other sources of feedback. In this study, we use eye tracking data to inform a collaborative filtering model for movie recommendation providing an improvement over the click-based implementations and additionally analyze the area of interest (AOI) duration as related to the known information of click data and movies seen previously, showing AOI information consistently coincides with these items of interest.

READ FULL TEXT
research
03/09/2018

Collaborative Filtering with Graph-based Implicit Feedback

Introducing consumed items as users' implicit feedback in matrix factori...
research
07/04/2023

Understanding User Behavior in Carousel Recommendation Systems for Click Modeling and Learning to Rank

Carousels (also-known as multilists) have become the standard user inter...
research
08/30/2017

A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems

Between matrix factorization or Random Walk with Restart (RWR), which me...
research
02/21/2018

Improving Recommender Systems Beyond the Algorithm

Recommender systems rely heavily on the predictive accuracy of the learn...
research
05/09/2021

Stronger Privacy for Federated Collaborative Filtering with Implicit Feedback

Recommender systems are commonly trained on centrally collected user int...
research
04/29/2018

Neuroscientific User Models: The Source of Uncertain User Feedback and Potentials for Improving Recommendation and Personalisation

Recent research revealed a considerable lack of reliability for user fee...
research
05/01/2018

Fixation Data Analysis for High Resolution Satellite Images

The presented study is an eye tracking experiment for high-resolution sa...

Please sign up or login with your details

Forgot password? Click here to reset