将用户资料与就业机会相匹配
Matching user profiles with employment opportunities
我目前正在开发一种软件，可以根据用户个人资料将用户与工作联系起来。我 运行 对职位描述进行文本分析，并从中得出重要的关键词。我还从他们的个人资料中收集了用户信息。将工作与用户配置文件相匹配似乎是一项具有挑战性的任务。是否有任何基于机器学习的算法可用于配对？
好的，基本上，每个职位描述都有关键字，然后您有某种文本数据（用户配置文件），您可以尝试将这些关键字与之匹配。
由于您的训练数据（用户配置文件）未标记，supervised learning will not help you here. Unsupervised learning (clustering) could maybe help you in finding a certain patterns (keywords) from a loads of user profiles, but you would certainly need to experiment with different sorts of techniques (such as gaussian mixture models 等）并观察可能的模式。
您可以做的更简单的事情是 derive/find 每个用户个人资料的关键字（换句话说，确定用户个人资料中还存在多少您的工作关键字），然后比较它们之间的距离在职位描述和用户个人资料中使用 cosine similarity. You would then only need to determine the minimal angle threshold. This would be a parameter to play with. Of course you would need to vectorize your text data using bigrams or similar; if you use python there already is feature extraction in scikit). You could possibly also use tf-idf 矢量化器，但使用一些沉重且确定的单词停止列表。
我目前正在开发一种软件，可以根据用户个人资料将用户与工作联系起来。我 运行 对职位描述进行文本分析，并从中得出重要的关键词。我还从他们的个人资料中收集了用户信息。将工作与用户配置文件相匹配似乎是一项具有挑战性的任务。是否有任何基于机器学习的算法可用于配对？
好的，基本上，每个职位描述都有关键字，然后您有某种文本数据（用户配置文件），您可以尝试将这些关键字与之匹配。
由于您的训练数据（用户配置文件）未标记，supervised learning will not help you here. Unsupervised learning (clustering) could maybe help you in finding a certain patterns (keywords) from a loads of user profiles, but you would certainly need to experiment with different sorts of techniques (such as gaussian mixture models 等）并观察可能的模式。
您可以做的更简单的事情是 derive/find 每个用户个人资料的关键字（换句话说，确定用户个人资料中还存在多少您的工作关键字），然后比较它们之间的距离在职位描述和用户个人资料中使用 cosine similarity. You would then only need to determine the minimal angle threshold. This would be a parameter to play with. Of course you would need to vectorize your text data using bigrams or similar; if you use python there already is feature extraction in scikit). You could possibly also use tf-idf 矢量化器，但使用一些沉重且确定的单词停止列表。