Tagged Questions

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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13 views

How to categorize classifiers and matrix factorization methods?

I have a classification problem which is solved by a variety of methods. Among the methods are unsupervised methods, traditional classifiers and a supervised matrix factorization methods. The problem ...
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0answers
17 views

Can we use kNN and k-mean at a same time?

I Get dataset of neighbours using kNN and then I want to apply k-mean on that dataset. By using this, is it possible that I get more accurate result? Is it logically correct that use kNN and then ...
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28 views

Examples for Attacks on Recommender Systems [on hold]

It is known, that recommender systems based on CF can be attacked by injecting fake profiles. Are there any documented, real-world instances of attacks, that are known to the public, i.e. what the ...
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18 views

Positive and negative examples in Rocchio-based recommender

I am exploring the usage of Rocchio-based recommenders in e-commerce and news portals and trying to wrap my head about the concept of a negative rating. Often in e-commerce or news portals there is no ...
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1answer
10 views

Personalized search models

Many companies are using this term to describe the way that research results appear differently from user to another. I am thinking of ways that these personalized search models are built without. I ...
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0answers
39 views

Incremental SVD Recommendation System

I have the following Octave/Matlab code to compute an SVD-like matrix-decomposition: ...
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12 views

Practical problem computing de k-nearest neighbors in CF?

I’m trying to apply de knn to a very dynamic system where users (like/dislike) items very frequently and new items became available all the time. My question is how often should the algorithm ...
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0answers
20 views

What does it mean when Recommender Systems are making recommendations based on “static” data as opposed to “dynamic” recommender systems?

Much of new trends in recommender systems are based on giving recommendations taking into account changing user/services/items preferences or requirements. They mention, traditional recommenders make ...
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0answers
24 views

Can collaborative filtering be cast as a classical regression problem?

Having the Netflix challenge in mind: collaborative filtering is typically presented as a matrix dimension reduction. My question is how does the problem relate to classical regression (supervised ...
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3answers
45 views

The role of the bias terms in matrix factorization formulas?

I'm reading about matrix factorization for recommender systems. A basic matrix factorization model would be something like: $(p_i \times q_j ) + b_i + b_j$. That formula would compute the rating for ...
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0answers
24 views

The name data sparsity in different applications

I am recently surveying the techniques or algorithms which handle the data sparsity problems in various fields. And I find quite similar name "data sparsity" or "sparse data" is used including the ...
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1answer
25 views

Pairwise compatibility metric

I work at a company that sells clothes, and I've had good results with using cosine similarity to determine which products are "similar" to each other simply based on who owns them. I wanted to take ...
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0answers
49 views

Profile variable in collaborative filtering

I'm trying to create a recommendation system based on purchases. I did some tests and I found that for some groups of customers, the recommender works very well, but not for others. How can I ...
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29 views

Propensity in linear models and bilinear regression models

I'm reading this paper about matrix factorization. In the paper they want to combine the features of the nodes in the model (page 6). First they illustrate the simple idea of combining the features of ...
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1answer
40 views

Intuition behind matrix factorization formulations?

I'm reading this paper about matrix factorization. In the paper they propose to use this factorization for the adjacency (or similarity) matrix $G$ using the following formulation: $G = U \Lambda U^T$ ...
1
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1answer
49 views

Updating SVD in Recommender Systems for change in ratings

I have read that there are projection based methods to accomodate for new user's ratings or for the ratings for a new item in SVD. However, I want to know how to update my feature space for change in ...
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0answers
58 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?
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2answers
22 views

Assumption behind few latent features in recommender systems?

I know in recommender systems you have a rating matrix and then you factorize this matrix into two matrices and then learn those matrices with gradient descent. In those matrices we specify the number ...
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1answer
48 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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1answer
35 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 user's have "liked" i.e. all non-missing data has the same numeric value. Is it possible for me to using matrix ...
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0answers
70 views

Spectral clustering using RBF Kernel function in R

I have extracted user-features and item features in my recommender system using a modified SVD approach built on ALSE (loosely based on Yehuda Koren's paper). I now want to cluster items not directly ...
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0answers
23 views

Adding new item to recommender's options? [duplicate]

My shop has 20-items with a recommender system on top which analyses history of purchases and recommends items to buy each time customer returns. Now I want to add an additional item to choose from, ...
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1answer
80 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 ...
2
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2answers
99 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 ...
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0answers
40 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 ...
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0answers
43 views

Calculate Weights in pearson Correlation

I have user, movie data for recommendation purpose, where some of the users have rated relatively very fewer movies in compare to other users.I learned that while using Pearson Correlation to ...
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0answers
86 views

SVD application for a Boolean sparse Matrix

Basically, I am trying to have a recommender system based on SVD for a Boolean utility matrix. ie If at all some entries are present in the utility matrix, they will be 1 (I made it pseudo-implicit ...
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1answer
173 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 ...
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0answers
73 views

graphical methods / deep architectures for collaborative filtering

Having read "Restricted Boltzmann Machines for Collaborative Filtering" (Salakhutdinov et. al. 2007), I'm wondering if there has been any follow-up work on applying graphical and/or deep architectures ...
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0answers
36 views

Evaluating methods for news recommendation from an online experiment

I have data from an experiment that evaluated 3 methods for recommendation of news articles based on their content. The experiment showed an article that the user read and 6 recommendations presented ...
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0answers
28 views

Collaborative filtering for real values prediction

I have a data with users performances (scale between 0 to 1) on different tests and would like to test the accuracy of prediction when the data is sparse by removing randomly some records. In other ...
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0answers
124 views

What's wrong with my Kernel algorithm (Kernel SVD)?

I have a user-item matrix $A$ as data input, which is a sparse matrix containing a large number of missing values (as zeros). Each row is a user, and each column is an item. Generally, I am conducting ...
2
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0answers
79 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: ...
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0answers
39 views

How to optimize a SVD recommender regarding number of factors?

I am using R to do recommendation based on pureSVD. It is basically to choose the number of factors and then SVD the user-item matrix and then restore the matrix and provide top-N recommendations for ...
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0answers
27 views

Recommending Items based on Multiple Similarity Metrics

I'm building a recommendation systems that suggests items to a user based on items chosen by similar users. It's similar to collaborative filtering, although I am using multiple dimensions to describe ...
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0answers
37 views

Recommendation System with Graph Database

Hi I'm looking into creating a recommendation system based on a graph database for an ecommerce website. Basically something like amazon, "if my customer's have bought these products recommend some ...
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0answers
28 views

how to compare a user-based collaborative filtering with item-based

Apart from prediction accuracy, what are good evaluation metrics for comparing user-based and item-based collaborative filtering
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0answers
26 views

Collaborative Filtering, matrix decomposition, and incorporate various kinds of data

Just about every matrix factorization (e.g., SVD++) has some matrix that includes a n users and m (e.g.,) ratings. Here is my question, how do you include information like demographic information, ...
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0answers
43 views

clustering in Item-based collaborative filtering

I read about item-based collaborative filtering in the paper from Sawar et al. I want to apply clustering on items to find the most similar items and then apply the prediction. Is this a good ...
2
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2answers
206 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 ...
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0answers
125 views

The difference between SVD and SVD++

What if any is the connection between SVD (the one you learn about in your linear algebra course) and SVD++ (the one from the Netflix prize)? I know they both want to find latent factor spaces. But ...
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2answers
118 views

Compute the user and item features in SVD++

I have a sparse matrix. There is lots of missing data. Hence, I can't use SVD naively. I read Koren's SVD++ paper. I'm confused as to how the $q_i$ and $p_u$ vectors are determined. $q_i^Tp_u$ is ...
2
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1answer
43 views

What does “aspect model” refer to in machine learning

Hopefully this is the right place to ask my question. I am reading this paper about cold-start recommendations: http://dl.acm.org/citation.cfm?id=1352837 the expression "aspect model" is used a lot ...
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0answers
39 views

Confidence estimation for data points in a recommender system

I have a 100 by 100 matrix. Each cell is either 0, 1, or missing (denoted NA). Rows denote 'users' and columns denote 'items'. My goal is to impute the missing values, and provide a confidence level ...
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0answers
44 views

Cosine Distance and Recommender Systems

Suppose that a computer is rated according to three numerical features: processor speed, disk size, main-memory size. Consider three computers $A,B$ and $C$ with values: $$A(3.06,500,6)$$ $$B(2.68, ...
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0answers
17 views

How to find communities?

I know how to implement community detection algorithms in code. Of course, I know how to google. I know how to find groups on social networks. But I feel like it's only tip of an iceberg. Sometimes ...
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0answers
79 views

What is a good and efficient algorithm for a content based recommender?

I want to build a content based recommender in a restricted environment regarding cpu power and memory (to be specific: a mobile device, but it is not acceptable to build the recommender on a remote ...
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votes
1answer
94 views

What does ItemAverage Recommender do in Mahout

The description sounds to me as if it makes a MostPopular recommendation. But the MostPopular recommendation I did myself got much better results. So what does this recommender really return? It is a ...
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0answers
391 views

Is there an implementation of Item-Item Collaborative Filtering in R? [closed]

I was reading about item-item collaborative filtering at: http://guidetodatamining.com/guide/ch3/DataMining-ch3.pdf But I did't find an implementation in R of this algorithm. Is there one?
1
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1answer
312 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 ...