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.

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30
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5answers
38k views

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 ...
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3answers
6k views

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 ...
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4answers
14k views

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 ...
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4answers
2k views

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 ...
14
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3answers
4k views

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?
14
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3answers
1k views

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 ...
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7answers
4k views

Recommendation for a book about recommender systems

Can you recommend a book with good information that can be applied to developing a recommender system?
13
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2answers
455 views

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....
13
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1answer
721 views

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 ...
12
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3answers
6k views

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 ...
11
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1answer
1k views

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 ...
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3answers
3k views

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 (...
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2answers
4k views

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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2answers
3k views

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 ...
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2answers
4k views

Does it make sense to measure recall in recommender systems?

Assume I've built a recommender system that (given say movie rankings or whatever of many users) will produce a list of 10 recommended movies for each user to watch. Imagine that I also have some ...
8
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2answers
3k views

Matrix Factorization Model for recommender systems how to determine number of latent features?

I am trying to design a matrix factorization technique for a simple user-item, rating recommender system. I have 2 questions about this. First in a simple implementation that I saw of matrix ...
7
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1answer
4k views

Evaluating recommender systems with (implicit) binary ratings only

I'm analyzing a set of news articles and user libraries. User library is the set of news articles shared by one user. Obviously, the rating is 1 (the article is in user's library) and 0, otherwise. I ...
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2answers
1k views

Matrix Factorization algorithms for Recommender Systems

I need to learn about Matrix Factorization for recommender systems, so I downloaded this paper https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf but I found it too shallow. It ...
6
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1answer
1k views

Matrix factorization in recommender systems: adding a new user

I estimate ratings in a user-item matrix by decomposing the matrix into two matrices P and Q and then using gradient descent to ...
6
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2answers
921 views

Are there any probabilistic models for graph-based recommender systems?

All I can find now is somehow based on random walks or graph kernels, which is nice, but I want to have a more or less solid probabilistic foundation for my recommender system for bounds and ...
6
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3answers
3k views

Collaborative filtering and implicit ratings; normalization?

I would like to use the time a user spends viewing an article as an implicit rating of how much the user likes the article. My question is how do I normalize this information across all users. At ...
6
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3answers
123 views

How to determine a user's favorite content producer from individual ratings?

Consider the following scenario: Alice subscribes to a video rental service that allows her to watch movies. Every time A watches a movie, she rates it either thumbs up (1) or thumbs down (0), and ...
6
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1answer
321 views

Implicit Feedback Factorization Machines : Format of Input and Recommendations

I am looking at methods for making product recommendations to customers based on attributes of the customer (e.g. demographics) and past product interactions (e.g. did or did not buy). There are ~250 ...
6
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1answer
908 views

Practical collaborative filtering application for large database

I’m designing an item-based collaborative filtering for a large database with over 100,000 items. My question is how the whole process works in practice since the algorithm takes a long time to ...
5
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1answer
3k views

What metric should I use for assessing Implicit matrix factorization recommender with ALS?

I'm currently implementing a recommender system using implicit ratings (time spent on an article) but I'm wondering what would be the proper metric to assess the system. MSE doesn't seems to fit to ...
5
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3answers
399 views

Simple recommender system - where to start?

Without going into specifics, I'm currently working on a system that involves 20-25 questions being answered as either Green, Yellow, Orange or Red. After completing a subset of these questions (many ...
5
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1answer
388 views

Conditional Logit for recommender systems?

Are conditional multinomial logits used for recommendation engines? Although they are commonly used in econometrics, I've never heard it used or discussed in the context of recommender systems. ...
5
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2answers
1k views

SVD in movie recommendation

Assume that there is a 5 $\times$ 6 matrix that records the ratings of six users on five movies. I have computed the singular value decomposition (SVD) for such a matrix. Suppose I add another ...
5
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1answer
805 views

What is the advantage of non-negativity in matrix factorization?

I am wondering why matrix factorization techniques in the machine learning domain almost always expect the provided matrix to be non-negative. What is the advantage of this constraint? Background: I ...
5
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1answer
442 views

What is the standard procedure for evaluating a user-based CF algorithm with a dataset offline?

I have read some papers and other materials about the evaluation of recommender systems (RS). Most of them discuss the various properties of RS (e.g. accuracy, diversity, etc.), and metrics for ...
5
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2answers
895 views

Finding similar items based on user likes and dislikes

I have data on content-based recommendations of movies and their attributes. Suppose a user likes x,y and z and also dislikes c and d movies. I want to predict movies that he will like based on his ...
5
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1answer
451 views

Predict user behaviour with constantly changing input variables

How to work on building an engine for a website wherein we want to score/recommend stuff based on her different activities, like the music she rated or the article she read, or whether email ...
5
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1answer
2k views

Proper way to use NDCG@k score for recommendations

Currently I am building a recommender system and using ranking metrics to verify its performance. I am using the NDCG@k score. Today I was experimenting and I realized that I might be calculating the ...
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0answers
2k views

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 ...
4
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2answers
4k views

Generating recommendations using matrix multiplications

The Mahout In Action (Chapter 6) book contains a recommendation method based on matrix multiplication that uses co-occurrence data (C) in combination with user preferences (U) to generate user ...
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3answers
4k views

Using advance optimisation techniques for collaborative filtering systems, is it possible?

Is it possible to use advance optimization(L-BFGS, Conjugate gradient) for a collaborative filtering system vs just using gradient descent? I ask this because of the need to calculate both X and theta ...
4
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1answer
3k views

Evaluating matrix factorization algorithms for Netflix

I've been trying to implement Simon Funk's movie recommendation algorithm explained here. I understand how the user and item factors are computed. However the evaluation method is not clearly ...
4
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1answer
713 views

Understanding how recommender systems work for data without ratings

I am trying to build a recommender system for a supermarket and do not really have any ratings for any product apart from the number of purchases of each item. A sample of the data is like: ...
4
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4answers
617 views

What are good introductory papers on recommender systems?

I am beginning to build a recommendation system. I have users on a website and they purchase services, so I'll recommend services that commonly go along - i.e. are purchased by a single user (not ...
4
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2answers
1k views

Matrix Factorization Recommendation Systems with Only “Like” Ratings

I'm trying to build a recommendation system, but I only have data on what my users have "liked", i.e. all non-missing data has the same numeric value. Is it possible for me to use matrix ...
4
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1answer
866 views

What does the parameter $\alpha$ do in the Jaccard method for binaryRatingsMatrix in R recommenderlab?

What is the role of the parameter 'alpha' in the recommenderlab R package's use of Jaccard method in the recommender model for ...
4
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1answer
5k views

How to set preferences for ALS implicit feedback in Collaborative Filtering?

I am trying to use Spark MLib ALS with implicit feedback for collaborative filtering. Input data has only two fields userId and ...
4
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1answer
1k views

Recommendation system without rating, clicks, views, etc

I need to develop a recommendation system for a relatively new retail website. They mainly sell electronics items. Currently, the only data that is available is the transaction data (user, item, ...
4
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2answers
3k views

Recommendation using Random Forest [closed]

I am trying to write my own recommender system. I have data set of user-item rating matrix. But I do not have profile information about either items or users. I already built pure CF using cosine and ...
4
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1answer
611 views

What makes the recommendation problem unable to be solved by traditional machine learning algorithms directly?

We have collaborative filtering and content based algorithms there for recommendation. What stops traditional algorithms from directly being used to find missing values in the Utility matrix ...
4
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1answer
1k views

Item-based collaborative filtering – Can you add demographic information to initial user×item matrix?

I am building an item-based collaborative filter recommendation system. I have a matrix of users and items, which in this case, are products that were either bought or not (i.e., binary: ...
4
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1answer
208 views

How to Deal with Categorical Variables that Allow Selection of Multiple Values per Observation?

Say you are dealing with a movie database that has movies and their genres. Genre is a categorical variable but each movie can belong to more than one genre. For example, Movie A may be Comedy and ...
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0answers
45 views

Who will follow who based on tags?

Suppose users in a system like a social network are described by a number of tags. The number of tags can be assumed to be less than 10. Example John: funny musician geek professor Peter: skinny ...
4
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0answers
639 views

Stratified Cross-Validation with Collaborative Filtering

My dataset consists of binary preferences ($0$ or $1$) given by users on items like this: User-ID | Item-ID | Preference If a user has not given a preference to an item, then it is not in the ...
4
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0answers
76 views

Predicting user selections based on similar user

Lets say you give a set of users a set of polls, or give them a choice of foods to eat, or let them listen to a group of songs (guess like pandora). So looking at the choices that all the users make ...

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