cosine-similarity

I have a scenario where i have retreived information/raw data from the internet and placed them into their respective json or .txt files. From there on i would like to calculate the frequecies of each term in each document and their cosine similarity by using tf-idf. For example: there are 50 different documents/texts files that consists 5000 words/strings each i would like to take the first word from the first document/text and compare all the total 250000 words find its frequencies then do so for the second word and so on for all 50 documents/texts. Expected output of each frequecy will be

I'm working on a high-dimensional problem (~4k terms) and would like to retrieve top k-similar (by cosine similarity) and can't afford to do a pair-wise calculation. My training set is 6million x 4k matrix and I would like to make predictions for 600k x 4k matrix. What is the most efficient way to retrieve the k-similar items for each item in my 600k x 4k matrix? Ideally, I would like to get a matrix which is 600k x 10 (i.e., top 10-similar items for each of the 600k items). ps: I've researched the SO website and found almost all "cosine similarity in R" questions refer to cosine_sim(vector1,

For a Recommender System, I need to compute the cosine similarity between all the columns of a whole Spark DataFrame. In Pandas I used to do this: import sklearn.metrics as metrics import pandas as pd df= pd.DataFrame(...some dataframe over here :D ...) metrics.pairwise.cosine_similarity(df.T,df.T) That generates the Similarity Matrix between the columns (since I used the transposition) Is there any way to do the same thing in Spark (Python)? (I need to apply this to a matrix made of tens of millions of rows, and thousands of columns, so that's why I need to do it in Spark) You can use the

I have a dataset containing workers with their demographic information like age gender,address etc and their work locations. I created an RDD from the dataset and converted it into a DataFrame. There are multiple entries for each ID. Hence, I created a DataFrame which contained only the ID of the worker and the various office locations' that he/she had worked. |----------|----------------| | **ID** **Office_Loc** | |----------|----------------| | 1 |Delhi, Mumbai, | | | Gandhinagar | |---------------------------| | 2 | Delhi, Mandi | |---------------------------| | 3 |Hyderbad, Jaipur| -------

I am confused by the following comment about TF-IDF and Cosine Similarity . I was reading up on both and then on wiki under Cosine Similarity I find this sentence "In case of of information retrieval, the cosine similarity of two documents will range from 0 to 1, since the term frequencies (tf-idf weights) cannot be negative. The angle between two term frequency vectors cannot be greater than 90." Now I'm wondering....aren't they 2 different things? Is tf-idf already inside the cosine similarity? If yes, then what the heck - I can only see the inner dot products and euclidean lengths. I