A First Step toward Recommendations Based on the Memory of Users

Florian Marchal (1) , Sylvain Castagnos (1) , Anne Boyer (1)

1 KIWI - Knowledge Information and Web Intelligence

Most of recommender systems build their predictions by analysing the preferences of users. However, there are many situations, such as in intelligent tutoring systems, where recommendations of pedagogical resources should rather be based on their memory. So as to infer in real time and with low involvement what has been memorized by users, we highlight in the paper the link between gaze features and visual memory. We designed a user experiment where different subjects had to remember a large set of images. In the meantime, we collected about 19,000 fixation points. Among other metrics, our results show a strong correlation between the relative path angles and the memorized items. It is thus possible to predict the users' memory status by analyzing their gaze data while interacting with the system, so as to provide recommendations that fits their learning curve.

Mots clés: Learner modeling; Gaze data; Eye-Tracking; Visual memory; Recall

Domaines: Intelligence artificielle [cs.AI]

Florian Marchal, Sylvain Castagnos, Anne Boyer. A First Step toward Recommendations Based on the Memory of Users. 28th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2016), Nov 2016, San Jose, United States. ⟨10.1109/ICTAI.2016.0019⟩. ⟨hal-01421558⟩