KLECA: knowledge-level-evolution and category-aware personalized knowledge recommendation

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users' learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users' knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users' learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches.