# Questions tagged [matrix-factorisation]

In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices.

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### How to do binning in matrix data

I have some data like ...
1 vote
9 views

### understanding the factorisation machine formula

I am reading this tutorial about factorisation machines. I get the intuition behind it, compute the dot product between the (user/item)+(item/aux features)+(user/aux features). This dot product can ...
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### Why is using sigmoid in last layer of NN for General Matrix Factorisation showing better performance?

My understanding is that the example shown in Keras documentation https://keras.io/examples/structured_data/collaborative_filtering_movielens/ is the special case of a Neural Collaborative Filter ...
1 vote
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### How do the authors get this updating formula for all $\beta$ in $\beta$-divergence

I'm reading the paper Algorithms for nonnegative matrix factorization with the β-divergence by Cédric Févotte and Jérôme Idier. Package scikit-learn uses their algorithm for module sklearn....
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### Treating recommender systems as multiclass classification or binary classification problem

I'm thinking about the two following approaches for building a recommender system to recommend products using implicit data as a classifier: Treat it as a multi-class classification problem. The ...
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### Advantages of matrix factorization when the number of products is low

I'm building a recommender system where the number of products is rather low (around 50), and we can assume it'll stay the same for a long time. I'm looking at two different way of tackling the ...
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### Calculate implicit rating from streaming behaviour for Recommendation Engine

I have a dataset containing some user streams data for particular videos like below: ...
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1 vote
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### Non-negative Matrix Factorization for clustering

I'm learning to user NMF to do clustering. Based on the reading What is a good explanation of Non Negative Matrix Factorization? and https://iksinc.online/2016/03/21/what-is-nmf-and-what-can-you-do-...
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1 vote
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### Temporal train test split for recommender systems

When evaluating a collaborative filtering recommender system, it is practical to split the data temporally. However, by doing so, some users might be present in only either of the train or test set. ...
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1 vote
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### Matrix factorization how to initialize weights and biases?

I have a matrix factorization and I'm wondering how I should initialize its weights and biases. When getting prediction (recommendation), after computing a dot product and adding bias I want to use ...
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### What is the best model for a recommendation system using implicit ratings?

I have a similariy matrix that looks like this: I have a bunch of user vectors with 1s and 0s, with a 1 indicating that someone has clicked on an email (as part of a campaign) and zero to indicate ...
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### How to filter Items in Recommender Systems?

I have a Recommender System which recommends Articles based on Similarity from 3 Features, "Page-Title, Article Content, Tags". But some of the Articles are NSFW(Related to Adult Topics). I ...
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### Negative Latent Factors in Factorized Machines

I'm studing a specific implementation of a recommendation system leveraging on a factorization machine algorithm. For each person_id and ...
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1 vote
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### How do I approach this problem?

Let's say I have a dataset with multiple types of multiple ingredients ($salt_1$,$salt_2$, etc). Each $n\text{-th}$ variation of each ingredient vs flavor may be represented by an $n \times k$ matrix ...
1 vote
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### How do I perform K-Means clustering of the Olivetti Dataset

This Question pertains to Matrix Factorization and the full question is given below. Provide for k-means clustering of the Olivetti dataset the following visualizations: A scatter plot of the r = 2-...
766 views

### How to create a model and make predictions with LightFM?

I've been researching on how to develop a hybrid recommender system for a simple book dataset, the main goal is to use both explicit data (purchases) and latent factors (features) to make the ...
1 vote
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### Intuitive explanation of difference between PCA and SVD [closed]

Can someone explain the difference between SVD and PCA with real life example?
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### What is the scikit learn Non-negative Matrix Factorisation Coordinate Descent algorithm?

What is the scikit-learn Coordinate Descent (CD) algorithm for Non-negative Matrix Factorization (NMF)? The sklearn implementation of NMF has two different solvers, Coordinate Descent and ...
1 vote
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### Differences and similarities between nonnegative PCA and nonnegative matrix factorization

I have seen references in the literature to nonnegative principal component analysis (nPCA) and nonnegative matrix factorization (NMF). I have tried reading the papers on both of them but it is not ...
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1 vote
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### Mobile App Recommendation: How to get the rate of a specific user submit for a specific application [closed]

I have a mobile app recommendation project, so I need data set which has user-app matrix-rate. Actually, I want to know what rate does a specific user submit for a specific application. in other words,...
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### How exactly does matrix factorization help with collaborative filtering

We start with a matrix of user ratings for different movies with some elements unknow i.e the rating of a yet to be seen movie by an user. We need to fill in this gap. So How can you decompose or ...
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### What is local-NMF? How is it better than original NMF?

I am reading this paper, but don't really understand. Do the words "part-based" or "local" for non-negative matrix factorization (NMF) mean that the algorithm aims to factorize some specific parts ...
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1 vote
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### Prediction with multiple features (attributes) [closed]

I recently used a prediction algorithm to try to predict how much an user is willing to interact with an item using ALS (Matrix Factorisation) but I had to "condense" multiple attributes into one, ...
1 vote
112 views

### Text generation using Tensor Factorization

Text generation is well studied using Markov chains or NNs, but I am not aware of any works to word sequence prediction in terms of subspace learning. Treating phrases or sentences as temporal data ...
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### Factorization Machine - prevent over fitting

I was recently asked this question in an interview and wondered what the answer would be - "How do Factorization Machines get around the overfitting problem when using second-order interactions?"
1 vote