Adapting to User Interest Drifts for Recommendations in Scratch
Youhua Jiang, Siyi Yan, Peng Qi, Yan Sun · 2020
Scratch is a popular programming platform with plenty of learning resources. However, it is quite difficult for users to find suitable resources. In this paper, in order to provide the resources which the users require, we propose a Scratch Recommendation Framework Adaptive to User Interest Drifts (SRFA-UID). First, a user interest drifts model is designed, which adopts the time decay factor and the weights of operation behaviors to track users' dynamic interest. Then, on the basis of users' current and historical interest, we calculate their combined user similarity. Next, we present a novel two-hop-algorithm to update users' friend community. Considering the preferences of the whole friend community, the Computational Thinking (CT) skills of users and the impact factors of items, we put forward a recommendation function to obtain items that are related to users. For an item, the function can calculate its F value to determine if we can recommend it to the users. Experimental results show that SRFA-UID performs better than other state-of-the-art methods in the Scratch dataset.