Questions tagged [cosine-distance]

A measure of the angular distance between two vectors. Usually defined as 1-(cosine similarity).

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What ways can i find two similar sets of customers use KNN?

I have a study where i want to find users similar to a set of users (SEED). My data looks like a pivot by customer e.g. sample of SEED looks like (note i drop cust_id): ...
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Cosine-like alternative to Mahalanobis distance

I would like to have a distance measure that takes into account how spread are vectors in a dataset, to weight the absolute distance from one point to another. The Mahalanobis distance does exactly ...
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Document Similarity with User Preference

To measure the similarity between two documents, one can use, e.g. TF-IDF/Cosine Similarity. Supposing that after calculating the similarity scores of Doc A against ...
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Tag texts using predefined keywords based on the importance

I want to tag a list of texts using predefined keywords ex: keyword1, keyword2, keyword3. I ...
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Can siamese model trained with euclidean distance as distance metric use cosine similarity during inference?

If I have 3 embeddings Anchor, Positive, Negative from a Siamese model trained with Euclidean distance as distance metric for triplet loss. During inference can ...
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NLP Interview Coding Task

Please comment on the following NLP Interview Coding Task that I have prepared for the candidates on Data Science NLP position that I am looking for. The goal is to check candidate understanding of ...
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Conceptual question about cosine similarity and clustering algorithms for word embeddings

Is the following statement true? https://stats.stackexchange.com/q/256778 The value of cosine similarity between two terms itself is not indicator whether they are similar or not. If yes then how is ...
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Which approach is beneficent for identifying the fake news detection?

The problem is to identify the fake news detection, As this is text classification problem . Constraints are basically that we cannot use traditional machine learning and deep learning approaches. If ...
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K-means++ with cosine distance

I am wondering how to implement k-means++ with cosine distance, acording to quote below (wikipedia), which says, that distance needs to be squared. But with square is lost direction of distance which ...
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Cosine similarity between sentence embeddings is always positive

I have a list of documents and I am looking for a) duplicates; b) documents that are very similar. To do so, I proceed as follows: Embed the documents using paraphrase-xlm-r-multilingual-v1. ...
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Plotting cosine similarities in 3d space from word embeddings

I'm working on a project in which I want to estimate biases from a large corpus of newspaper articles using word2vec. Following this and this paper, biases are calculated by constructing dimension x ...
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How to compute similarity matrix for strings efficiently? [duplicate]

Here I'm trying to compute similarity between 1000 cross 10000 strings (using Levenshtein distance), I'm using a dataframe approach where you just need to compare n(n-1)/2 comparisons instead of n*n. ...
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applicability of relative similarity computation

I've computed the cosine similarity between a & b (=x) and ...
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Inner working behind combining two distance function as one function for similarity measure

I am comparing two images and for this I am testing various similarity function. For my case, Euclidean works much better than cosine(20% difference). However, I tried to combine two distance function ...
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1 answer
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String Matching [closed]

I have to work on 2 datasets where I have to find out the duplicates in addresses present in both the dataset. I am a bit confused that which one of the Levenshtein distance or cosine similarity, I ...
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Cosine vs Manhattan for Text Similarity [closed]

I'm storing sentences in Elasticsearch as dense_vector field and used BERT for the embedding so each vector is 768 dim. ...
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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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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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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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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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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 ...
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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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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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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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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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2 votes
1 answer
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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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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 ...
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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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2 answers
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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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-1 votes
1 answer
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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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2 votes
1 answer
476 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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1 vote
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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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3 answers
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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 ...
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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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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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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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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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5 votes
1 answer
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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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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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4 votes
4 answers
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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 ...
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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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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) ...
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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 ...
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1 vote
1 answer
630 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 ...
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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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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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1 vote
1 answer
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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 ...
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4 votes
1 answer
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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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2 votes
1 answer
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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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3 answers
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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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