Sparse Information Filtering for English Language Repositories Using Multilevel Interactive Attention Mechanism (Informatica, 2025)
English learning resources involve a large amount of language knowledge and semantic information, and users' English learning needs are multidimensional, and these needs will dynamically change with the learning process and time. It is prone to insufficient feedback and evaluation information, with a large amount of sparse information, making it difficult to accurately analyze user learning resource preferences. How to capture and adapt to the multi-dimensional demand characteristics of users in real-time is a challenge faced by recommendation systems. To this end, research is conducted on a sparse information filtering recommendation algorithm for English resource libraries that integrates multi-level interactive attention mechanisms. Filter sparse interval data through FCM membership threshold (0.2