协作主题建模的简单Python实现? [英] Simple Python implementation of collaborative topic modeling?

问题描述

我碰到了这两篇论文，它们结合了协作过滤(矩阵分解)和主题建模(LDA)，根据用户感兴趣的帖子/文章的主题词向用户推荐相似的文章/帖子.

I came across these 2 papers which combined collaborative filtering (Matrix factorization) and Topic modelling (LDA) to recommend users similar articles/posts based on topic terms of post/articles that users are interested in.

论文(以PDF格式)为: " 用于推荐科学文章的协作主题建模 "和 " 协作推荐GitHub存储库的主题建模 "

The papers (in PDF) are: "Collaborative Topic Modeling for Recommending Scientific Articles" and "Collaborative Topic Modeling for Recommending GitHub Repositories"

新算法称为协作主题回归.我希望找到一些实现此功能的python代码，但无济于事.这可能是一个长镜头，但是有人可以显示一个简单的python示例吗?

The new algorithm is called collaborative topic regression. I was hoping to find some python code that implemented this but to no avail. This might be a long shot but can someone show a simple python example?

推荐答案

这应该可以帮助您入门(尽管不确定为什么尚未发布):

This should get you started (although not sure why this hasn't been posted yet): https://github.com/arongdari/python-topic-model

更具体地说: https://github.com /arongdari/python-topic-model/blob/master/ptm/collabotm.py

class CollaborativeTopicModel:
    """
    Wang, Chong, and David M. Blei. "Collaborative topic 
                                modeling for recommending scientific articles."
    Proceedings of the 17th ACM SIGKDD international conference on Knowledge
                                discovery and data mining. ACM, 2011.
    Attributes
    ----------
    n_item: int
        number of items
    n_user: int
        number of users
    R: ndarray, shape (n_user, n_item)
        user x item rating matrix
    """

看起来很简单.我仍然建议至少查看gensim. Radim在出色地优化该软件方面做得非常出色.

Looks nice and straightforward. I still suggest at least looking at gensim. Radim has done a fantastic job of optimizing that software very well.

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