Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [recommender-system]

A recommendation engine tries to predict how much a user will enjoy certain goods (movies, books, songs, etc) and makes recommendations. They are often used by online vendors to suggest new purchases.

A recommendation system, or recommendation engine, is an automatic tool that recommends goods to a specific user. There are many examples on online commercial websites: Netflix or IMDB recommends movies, Amazon recommends books any many more, etc. The input of such algorithms are most commonly the past purchases of the user, the rating the user gave to other goods, the items that have been purchased together ("customer who bought this also bought..."), the browsing history, etc.

See also Wikipedia.

Reference: adapted from Stack Overflow SE.

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How do I use the SVD in collaborative filtering?

I'm a bit confused with how the SVD is used in collaborative filtering. Suppose I have a social graph, and I build an adjacency matrix from the edges, then take an SVD (let's forget about regularization, learning rates, sparsity optimizations, etc),…
Vishal
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What happens when you apply SVD to a collaborative filtering problem? What is the difference between the two?

In Collaborative filtering, we have values that are not filled in. Suppose a user did not watch a movie then we have to put an 'na' in there. If I am going to take an SVD of this matrix, then I have to put some number in there - say 0. Now if I…
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Difference between Factorization machines and Matrix Factorization?

I came across the term Factorization Machines in recommender systems. I know what Matrix Factorization is for recommender systems but never heard of Factorization Machines. So what's the difference?
Jack Twain
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Meaning of latent features?

I'm trying to understand matrix factorization models for recommender systems and I always read 'latent features', but what does that mean? I know what a feature means for a training dataset but I'm not able to understand the idea of latent features.…
Jack Twain
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Item-Item Collaborative Filtering vs Market Basket Analysis

What is the basic difference between Item based Collaborative Filtering and Market Based Analysis? Is the latter a specialised case of the former?
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What statistical methods are there to recommend a movie like on Netflix?

I am looking to implement a dynamic model to recommend a movie to a user. The recommendation should be updated every time the user watches a movie or rates it. To keep it simple I am thinking of taking two factors into account: the past ratings of…
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Dynamic recommender systems

A Recommender System would measure the correlation between ratings of different users and yield recommendations for a given user about the items which may be of interest to him. However, tastes change over time so old ratings might not reflect…
andreister
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Recommendation for a book about recommender systems

Can you recommend a book with good information that can be applied to developing a recommender system?
B Seven
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State-of-the-art in Collaborative Filtering

I am working on a project for collaborative filtering (CF), i.e. completing a partially observed matrix or more generally tensor. I am a newbie to the field, and for this project eventually I have to compare our method to other well-known ones that…
Cupitor
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Converting a list of partial rankings into a global ranking

I'm working on something like the following problem. I have a bunch of users and N books. Each user creates an ordered ranking of all the books he's read (which is likely a subset of the N books), e.g., Book 1 > Book 40 > Book 25. Now I want to turn…
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SVD of a matrix with missing values

Suppose I have a Netflix-style recommendation matrix, and I want to build a model that predicts potential future movie ratings for a given user. Using Simon Funk's approach, one would use stochastic gradient descent to minimize the Frobenius norm…
Vishal
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Why is non-negativity important for collaborative filtering/recommender systems?

In all modern recommender systems that I have seen that rely on matrix factorization, a non-negative matrix factorization is performed on the user-movie matrix. I can understand why non-negativity is important for interpretability and/or if you…
graffe
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Collaborative filtering through matrix factorization with logistic loss function

Consider collaborative filtering problem. We have matrix $M$ of size #users * #items. $M_{i,j} = 1$ if user i likes item j, $M_{i,j} = 0$ if user i dislikes item j and $M_{i,j}=?$ if there is no data about (i,j) pair. We want to predict $M_{i,j}$…
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How does XGBoost/lightGBM evaluate ndcg metric for ranking

I am currently running tests between XGBoost/lightGBM for their ability to rank items. I am reproducing the benchmarks presented here: https://github.com/guolinke/boosting_tree_benchmarks. I have been able to successfully reproduce the benchmarks…
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How to create recommender system that integrates both collaborative filtering and content features?

I am creating a Recommender System and want to incorporate both the ratings of "similar" users and the features of the items. The output is a predicted rating [0-1].I am considering a Neural Network (to start with). So, the inputs are a combination…
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