Questions tagged [cosine-distance]
A measure of the angular distance between two vectors. Usually defined as 1-(cosine similarity).
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10 views
Matching documents from different sets with tfidf and cosine distance
I have two different set of documents S1, S2, with 30 text documents each.
Using some text representation method, such as tfidf ...
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0answers
20 views
how to calculate the cosine similarity between two files?
I am using spark and scala to implement an issue. files contain phrases or sentences. I want to use domain based method to calculate the cosine similarity between tags.I convert two files into a ...
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1answer
30 views
pairwise_distances with Cosine and weighting
Is there a way to get a weight into the
pairwise_distances(X, metric='cosine')
Potentially using **kwrds?
...
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0answers
14 views
Is there a way to calculate the cosine distance between 2 time series?
Let's say I have time series data of City A, City B, City C & City D that looks like this:
...
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1answer
46 views
Calculate the similarity between pairs of time series data
I have 5 pieces of time series data. It is the weekly sales of 5 different stores (A,B,C,D,E). There are no missing values. A quick visual inspection shows that these 5 pieces of time series data have ...
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0answers
27 views
Is normalizing term weight necessary when cosine similarity is used in retrieval?
When using cosine similarity in information retrieval, document vector length and query vector length are used for normalization. So if TF-IDF is used as a weighting function, then using raw frequency ...
2
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1answer
44 views
Cosine Similarity but with weighting for vector indexes
I am very new to NLP and although this seems like a basic question I don't know how to search for an answer online.
This is my problem: I have extracted and ranked keywords from 2 text sources:
A ...
1
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2answers
49 views
Evaluate document similarity / content-based recommender system
I'm planning on building a basic content-based recommender system with word2vec and cosine similarity.
The data consists of 300k documents in varying length.
How do I evaluate my model if I have no ...
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1answer
22 views
finding similarity of a new datapoint
I have built a recommendation engine using cosine similarity. When I want to find all the records similar to a given record that is already present in the dataset it works. Consider a case, a user ...
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1answer
32 views
Item-to-Item recommendation using DNN
I am new to DNN still learning, have a need to build item-to-item content based recommendation using DNN. For example, say I have a column of strings where each row represents a document I need to ...
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1answer
75 views
If i use use BERT embeddings for if cosine(sent1,sent2) > 0.9, then is it fair to assume s1 and s2 are similar
According to BERT author Jacob Devlin: I'm not sure what these vectors are, since BERT does not generate meaningful sentence vectors. It seems that this is doing average pooling over the word tokens ...
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0answers
24 views
Is the magnitude of a word vector correlated with the frequency of the word in a text?
In order to find the similarity between words, cosine similarity seems to be the most common measure to use. In a conversation that I had about this topic, someone mentioned that words that mean more ...
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1answer
156 views
Question about BERT embeddings with high cosine similarity
Under what circumstances would BERT assign two occurrences of the same word similar embeddings? If those occurrences are contained within similar syntactic relations with their co-occurrents?
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1answer
340 views
Semantic similarity between two or more sentences
I need to determine how similar sentences (in meaning) are to one another.
In order to do it, I have been considering an algorithm (cosine similarity) to determine the similarity between sentences. I ...
3
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1answer
535 views
Why is the cosine distance used to measure the similatiry between word embeddings?
While computing the similarity between the words, cosine similarity or distance is computed on word vectors. Why aren't other distance metrics such as Euclidean ...
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0answers
34 views
Is summing a cosine similarity matrix a good way to determine overall similarity?
I'm trying to similar research abstracts, so I'm using word embeddings to convert words into 1x768 vectors, so overall turning abstracts into embeddings with shape (#ofwords, 768). Cosine similarity ...
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34 views
How to compare cosine distances across two groups of words?
I am using a word2vec model based on Wikipedia corpus. I was looking for a way to quantify if two sets of words - s1= {a1, a2...}...
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1answer
222 views
Matrix of pairwise cosine similarities from matrix of vectors [closed]
I have a matrix of 200d vectors stored as follows:
$ X = \begin{pmatrix} \text{id}_1 & 0.5 & -2 & \dots & 10
\\ \text{id}_2 & -4 & 6 & \dots & -0.3
...
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1answer
138 views
How to get the probability/closeness of a sample belonging to a specific cluster?
I'm new to this so please let me know if my logic of comparing cosine similarity and k-means is incorrect
I got a set of ...
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0answers
19 views
Should I create a tfidf on a subset of a data set or use the whole corpus?
My goal in this project is to see if businesses on a list are currently customers within my organization. One piece of this involves producing a similarity score using cosine similarity on the names ...
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3answers
571 views
Fastest way for 1 vs all lookup on embeddings
I have a dataset with about 1 000 000 texts where I have computed their sentence embeddings with a language model and stored them in a numpy array.
I wish to compare a new unseen text to all the 1 ...
0
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1answer
159 views
How fit_transform, transform and TfidfVectorizer works
I'm a machine learning beginner and I tried to use the cosine similarity on fuzzy matching purpose. In the following example I want to compare 'data_dirty' with 'data_clean' :
When I have to ...
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0answers
20 views
NearestNeighbors testing
I have used
nbrs = NearestNeighbors(metric= 'cosine', algorithm='brute').fit(items_features)
distances, indices = nbrs.kneighbors(item_features)
to find some ...
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1answer
65 views
Understanding cosine distance with word vectors
I'm a new DL4J user, and I'm running all the works of Shakespeare through a Word2Vec neural net. I've got a pretty basic question about how to understand the results so far. In the below example, ...
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1answer
79 views
Approach to semantic similarity between documents
I was wondering what approach people would take, or point me in the right direction on this challenge I have set myself. I am pretty new at this, I have covered some area but want to expand my ...
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1answer
81 views
Evaluating the performance of a machine learned recommendation system
I have a set of resumes $R=\{{r_1,...,r_n\}}$, which I've transformed to a vector space using TF-IDF. Each resume has a label, which is the name of their current employer. Each of these labels comes ...
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1answer
19 views
TensorFlow: CosineDifference ObjFunc Constant throughout training
The following example is a simplified version of what I'm working on. I'm trying to find a neural network which minimises the cosine distance. The reason I have implemented my own cosine difference ...
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3answers
2k views
Cosine similarity vs The Levenshtein distance
Cosine similarity vs The Levenshtein distance
I wanted to know what is the difference between them and in what situations they work best?
As per my understanding:
Cosine similarity is a measure of ...
0
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1answer
69 views
Siamese networks vs Semantic similarity (may be gensim)
I am trying to understand the Siamese networks . In this vector is calculated for an object (say an image) and a distance metric is applied (say manhatten) on two vectors produced by the neural ...
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1answer
293 views
counter vector fit transform cosine similarity memory error
count_matrix = count.fit_transform(off_data3['bag_of_words'])
I have count_matrix shape with
count_matrix.shape
(476147, 482824)
...
2
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1answer
3k views
memory error in matrix cosine_similarity
I have (20905040, 7)
of a dataset to recommend 10 different product to the user
it could be larger than that but anyway I got memory error when processing the
...
1
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1answer
414 views
Elbow method for cosine distance
I have clustered vectors by cosine distance using nltk clusterer. If I understand correctly, Y axis for elbow method in euclidian distance would be the sum of every distance (squared) between centroid ...
0
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1answer
35 views
Document matching with more priority to certain features than others
I am working on recommendation systems wherein I need to match the similarity of 2 users. Now, I know that I can use Tfidf vectorizer to calculate the the cosine similarity score between them. But, ...
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1answer
3k views
Calculating cosine similarity between 3D arrays using Python
I have two matrices with multiple columns and three rows each. I calculated the cosine similarity (sklearn) but it gives the result as a matrix. How can I obtain one single value?
The matrices are the ...
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1answer
55 views
Checking TF-IDF Results
I am working with TF-IDF and cosine similarity to do document comparisons and given a document, which document in the data is the most similar. However, sometimes it returns a high similarity between ...
4
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1answer
766 views
word2vec word embeddings creates very distant vectors, closest cosine similarity is still very far, only 0.7
I started using gensim's FastText to create word embeddings on a large corpus of a specialized domain (after finding that existing open source embeddings are not performing well on this domain), ...
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1answer
5k views
Hierarchical clustering with precomputed cosine similarity matrix using scikit learn produces error
We want to use cosine similarity with hierarchical clustering and we have cosine similarities already calculated.
In the sklearn.cluster.AgglomerativeClustering documentation it says:
A distance ...
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3answers
1k views
Cosine similarity with arrays contaning NaN
I am trying to calculate a cosine similarity using Python in order to find similar users basing on ratings they have given to movies. As it can be expected there are a lot of NaN values.
I am using ...
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2answers
4k views
how to create word2vec for phrases and then calculate cosine similarity
I have just started using word2vec and I have no idea how to create vectors (using word2vec) of two lists, each containing set of words and phrases and then how to calculate cosine similarity between ...
2
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3answers
88 views
Match a two items from two different receipts
I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement).
Suppose I have ordered(PO) Wine:
White Wine
Red Wine
Rose Wine
And I receive ...
2
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1answer
38 views
When I would use a specific similarity coefficient over another?
Like using Jaccards over Dice.
I want real examples, of when I would prefer to use Jaccards, Dice, Cosine or any other similarity coefficient.
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1answer
1k views
cosine similarity between items (purchase data) and normalisation
I'm using IndexedRowMatrix which represents the products's user purchase behaviours and in order to build product recommendations, I use cosine similarity to calculate similarities between products. ...
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0answers
32 views
Best way to find dissimilarity in a 6x2 DataFrame?
I'm new to data science and am currently learning different techniques that I can do with Python. Currently, I'm trying it out with Spotify's API for my own playlists.
The goal is to find the most ...
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1answer
31 views
Can I sum up feature vectors of a user‘s collection?
I want to find items that are similar to items users already have in their collection. Every item has attributes, so I created feature vectors where every element of the vector represents an attribute ...
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3answers
712 views
clustering 2-dimensional euclidean vectors - appropriate dissimilarity measure
I've got a set of approx. 50 000 2-dimensional euclidean vectors which are connected with 20 groups, i.e. each group has approx. 2500 2-dimensional euclidean vectors. My data includes endpoints ...
2
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0answers
1k views
Clustering with cosine similarity with specific threshold (in Python)
Given :
1, either given the origin data matrix X , such as X = [[1,1,1,1],[1,1,-1,-1],[1,1,1,-1]]
or given the cosine similarity matrix of the original data X , ...
3
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1answer
3k views
How to find similar time series?
I've got a collection of yearly data (one value per year per category), and I'd like to find series that are most similar to one another.
Example data is here.
I don't know much about data science, ...
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1answer
26k views
cosine_similarity returns matrix instead of single value
I am using below code to compute cosine similarity between the 2 vectors. It returns a matrix instead of a single value 0.8660254.
[[ 1. 0.8660254]
[...
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4answers
12k views
Can I use cosine similarity as a distance metric in a KNN algorithm
Most discussions of KNN mention Euclidean,Manhattan and Hamming distances, but they dont mention cosine similarity metric. Is there a reason for this?
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2answers
690 views
Confusion with cosine similarity
In information retrieval when we calculate the cosine similarity between the query features vector and the document features vector we penalize the unseen words in the query.
Example if we have two ...
