Reducing cold start problems in educational recommender systems
Stanislav Kuznetsov, Pavel Kordík, Tomáš Řehořek, Josef Dvorak, Petr Kroha · 2016
Educational data can help us to personalise university information systems. In this paper, we show how educational data can be used to improve the performance of interaction-based recommender systems. Educational data is transformed to student profiles helping to prevent cold start problems when recommending projects to students with few user interactions. Our results show that our hybrid interaction based recommender boosted by educational profiles significantly outperforms bestseller recommendation, which is a mainstream recommendation method for cold start users.