# How to learn irrelevant words in an information retrieval system?

Right now my recommender system for information retrieval uses word embedding stogether with Tfidfs weights like written here: http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/

Using Tfidf improves results. But I have the problem that irrelevant keywords (high frequent words) still have a large impact. Can I learn a system such that it learns on which words to pay attention - preferred in an unsupervised way?

What can you suggest for a better information retrieval using word embedings?

If you are working with TF-IDF then it's important to experiment with min_df and max_df parameter. I guess you are on Python since you linked a Python tutorial. Here is the TF-IDF documentation and the related text to the above parameters.
You might find several rules of thumb on the web. Some of them suggest using a flat number on the min_df close to 5-7 documents and a percentage on the max_df about 80-85%. Maybe even lower. With this, you will be able to get rid of garbage, misspelt or unwanted tokens. Keep in mind that you need to try different combinations to get the right balance in your model.