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

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.