Sparse Information Filtering for English Language Repositories Using Multilevel Interactive Attention Mechanism

Abstract

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<μ<0.8), extract core English resource features, and then use a multi-level interactive attention mechanism to hierarchically extract preference features from the user layer (7 types of features such as age/interest) and resource layer (7 types of features such as listening/writing). After feature fusion, use Top-K method to calculate resource similarity and generate a recommendation list. Experiments have shown that on a dataset of 1500 resource items, 700 users, and 50000 ratings, the algorithm achieves significantly better performance in three key indicators: consistency in preference feature extraction (0.949-0.968), resource coverage (≥ 0.9), and conversion rate (96.83%) compared to the baseline model (LSTM model conversion rate 39.35%), and a 2.6-fold increase in detail page clicks. By dynamically capturing the user resource interaction relationship, the algorithm achieved an accurate proportion matching of 0.868 (with a deviation of 0.002) in the recommendation of listening resources, verifying its superiority in multi-dimensional dynamic demand scenarios.