Design and user behavior analysis of an English learning social platform based on digital entertainment content recommendation algorithm

The role of social networks in distance education cannot be ignored. Through social networking platforms, students can communicate and interact with other classmates, share learning experiences and problem-solving methods, and provide learning support and encouragement to each other. However, traditional English learning platforms have problems such as limited content and lack of interactivity. This article collects and analyzes learners' historical learning data, establishes learners' learning models, and learns about their learning habits, preferences, and other information. Subsequently, a collaborative filtering algorithm is used to match learners with other learners with similar learning behaviors and interest preferences, identify similar groups of learners, refer to the choices and preferences of learners who are similar to them, and recommend suitable learning resources for them. In addition to personalized recommendations, this article also designs a comprehensive set of social functions to promote interaction and knowledge sharing among learners. The results showed that the effectiveness and feasibility of the designed English learning social platform were verified through analysis and experimentation of actual user data. User feedback shows that the personalized recommendations and social functions provided by the platform have significantly improved learning outcomes and communication between learners.