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A Bayesian collaborative filtering approach to alleviating the Cold Start Problem in adaptive fact learning

Authors
Maarten van der Velde
University of Groningen ~ Experimental Psychology
Florian Sense
University of Groningen ~ Experimental Psychology
Dr. Jelmer Borst
University of Groningen ~ Artificial Intelligence
Hedderik van Rijn
University of Groningen, The Netherlands
Abstract

We present a method for mitigating the cold start problem in a computer-based adaptive fact learning system. Currently, learning sessions have a “cold start”: the learning system initially does not know the difficulty of the study material, resulting in a suboptimal start to learning.The fact learning system is based on a computational model of human memory and adaptively schedules the rehearsal of facts within a learning session. Facts are repeated whenever their activation drops below a threshold, ensuring that repetitions occur as far apart as possible, while still happening soon enough to encourage successful recall. Throughout the session, response times and accuracy are used to update fact-specific rate-of-forgetting estimates that determine each fact’s decay, and thereby its repetition schedule. When a learner first studies a set of items, the memory model uses default rate-of-forgetting estimates, leading to a suboptimal rehearsal schedule at the start of the session: easy facts are initially repeated too much, while difficult facts are repeated too infrequently.Here, we take a collaborative filtering approach to reducing the cold start problem. A Bayesian model, trained on rate-of-forgetting estimates obtained from previous learners, predicts the difficulty of each fact for a new learner. These predictions are then used as the memory model’s starting estimates in a new learning session.In a preregistered experiment (n = 197), we confirm that this method improves the scheduling of repetitions within a learning session, as shown by participants’ higher response accuracy during the session and better retention of the studied facts afterwards.

Tags

Keywords

memory
learning
cognitive modeling
bayesian modeling
user modeling

Topics

Cognitive Modeling
Bayesian Modeling
Learning
Memory Models
Discussion
New

Hi Maarten! Very cool stuff! You mention you are using Bayesian collaborative filtering to improve the estimates for single items at the start of the experiment. How did you decide to go for this method and have you compared it to more simple methods (e.g., just giving it a simple probability as starting point).

Marieke Van Vugt 0 comments

Nice paper! Did you look at how the improvement of recall varied with the original forgetting rate?

Dr. Stephen Mark Jones 1 comment