A Contextual-bandit approach on MIND Datasets for News Recommendation Systems.

A scalable two-stage news recommender that retrieves relevant candidates and reranks them using hybrid lexical and semantic features to optimize top-K recommendation quality.

SpringBoot和SSM实现基于协同过滤算法的个性化新闻推荐系统，使用了基于用户的协同过滤推荐算法，根据评分数据计算推荐，同时还使用了新用户喜好标签进行混合推荐，及将两种推荐结果全部输出，解决了冷启动和数据稀疏性问题。同时采用基于统计的热点推荐和相关推荐等。采用爬虫收集新闻数据实时更新新闻数据和推荐结果。

This project implements a news article recommendation system using collaborative filtering techniques. The system analyzes user interactions with various content items (news articles) to suggest new content that users might find interesting. The primary goal is to enhance user engagement by providing personalized recommendations.