协作主题建模的简单Python实现？

Question

我看到了这两篇文章，它们结合协作过滤(矩阵分解)和主题建模(LDA)，根据用户感兴趣的文章/文章的主题术语，推荐用户类似的文章/帖子。

论文(PDF格式)是："http://www.cs.columbia.edu/~blei/papers/WangBlei2011.pdf“和"http://www.cs.cmu.edu/afs/cs.cmu.edu/Web/People/norii/pub/github-ctr.pdf”。

新算法称为协同主题回归。我希望找到一些实现这个功能的python代码，但没有结果。这可能是一个很长的机会，但是有人能给出一个简单的python例子吗？

Answer 1

这应该会让您开始工作(虽然还不确定为什么还没有发布)：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
    """

看上去又好又直截了当。我仍然建议至少看看gensim。Radim在优化该软件方面做得非常出色。

Answer 2

一个非常简单的使用gensin的LDA实现。您可以在这里找到更多的信息：https://radimrehurek.com/gensim/tutorial.html

我希望它能帮到你

from nltk.corpus import stopwords
from nltk.tokenize import RegexpTokenizer
from nltk.stem import RSLPStemmer
from gensim import corpora, models
import gensim

st = RSLPStemmer()
texts = []

doc1 = "Veganism is both the practice of abstaining from the use of animal products, particularly in diet, and an associated philosophy that rejects the commodity status of animals"
doc2 = "A follower of either the diet or the philosophy is known as a vegan."
doc3 = "Distinctions are sometimes made between several categories of veganism."
doc4 = "Dietary vegans refrain from ingesting animal products. This means avoiding not only meat but also egg and dairy products and other animal-derived foodstuffs."
doc5 = "Some dietary vegans choose to wear clothing that includes animal products (for example, leather or wool)." 

docs = [doc1, doc2, doc3, doc4, doc5]

for i in docs:

    tokens = word_tokenize(i.lower())
    stopped_tokens = [w for w in tokens if not w in stopwords.words('english')]
    stemmed_tokens = [st.stem(i) for i in stopped_tokens]
    texts.append(stemmed_tokens)

dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

# generate LDA model using gensim  
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=2, id2word = dictionary, passes=20)
print(ldamodel.print_topics(num_topics=2, num_words=4))

(0，u‘0.066*动物+ 0.065*，+0.047*乘积+0.028*哲学’)，(1，u'0.085*。+0.047*产品+0.028*膳食+0.028*蔬菜‘)