In this project, we developed a game recommendation engine based on Steam user data. This engine recommends games to users based on how much time they spent playing previous games and their similarity to other video game players.
A content-based recommender that suggests games by combining features into a soup feature. Uses TF-IDF to compute similarity and deliver personalized recommendations.
Springboot协同过滤算法个性化游戏推荐系统，采用热门推荐，推荐评分高的游戏和收藏量多的游戏结合采用基于用户的协同过滤推荐算法（评分数据）和基于物品的协同过滤推荐算法（收藏数据）进行平均加权混合推荐，如果平均加权混合推荐没有推荐结果，采用热门推荐，推荐评分高的游戏和收藏量多的游戏，同时过滤当前登录用户已经评分、收藏的游戏。数据分析可视化，数据爬虫
The purpose of this project is to develop a recommender system based on content-based filtering in the Python programming language.
Game Analysis and Recommendation system
Analyzed multi-year clinical trial data and developed a game recommender system using PySpark and collaborative filtering techniques.
Game Recommendation Engine based on KRR principles
Game Recommender System is an AI-powered project developed as an assignment for TARUMT. It leverages machine learning techniques in Jupyter Notebook and Python to suggest video games tailored to user preferences.
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