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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25 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 ...
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13 views

evaluating the performance of item-based collaborative filtering for binary (yes/no) product recommendations

I'm attempting to write some code for item based collaborative filtering for product recommendations. The input has buyers as rows and products as columns, with a simple 0/1 flag to indicate whether ...
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17 views

Target definition by recommender

I want to use the Ranking factorization recommender from Graphlab. My question is, what is the exactly difference, when I use or don't use the target parameter in ...
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16 views

Item based collaborative filtering when items have been available for different lengths of time

I am attempting to use item based collaborative filtering for product recommendation. The matrix is all 1s and 0s based on whether or not a buyer purchased an item, and I am using cosine similarity to ...
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12 views

How to measure accuracy in content-based rec. system?

I am working on recommender system that makes prediction / recommendation with content-based and collaborative filtering techniques in one specific problem domain. I would like to measure if is better ...
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24 views

TF-IDF and weight of a rare term problem

I would like to use TF-IDF for content-based recommendation system for recommending movies. For computing document / movie similarities I would like to use tags (genres, actors, producers, director ...
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16 views

How to incorporate time dimension in a recommender engine?

I have very long history of user behaviour, when they choose to buy one of the 50 products. I want to take in account that if a user bought product1 two years ago and product2 yesterday, second ...
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40 views

Is precision in recommender system related to mean average error (MAE)?

A recommender system is being evaluated while increasing the neighborhood size. The highest precision was achieved between 10-15 neighbors(users) while the lowest MAE was in the range from 30-40 ...
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22 views

recommender - recommendation reasoning (get similarities )

I’m using recommenderlab to build a UBCF and produce recommendations. The process seems to be ok, and the predictions are making sense. Now,what I need to have, is the reasoning for each prediction, ...
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34 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 ...
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10 views

Personalization, user-adaptive and recommender systems

I am currently undergoing research into the field of systems that adapt content and layout depending on how the user uses the application. I am however puzzled as to the following terms as they are ...
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37 views

How to test a recommender system?

How can I test a feature-based collaborative filtering algorithm? The input user ratings are from 1 to 5 stars, but the predicted ratings are not in the same range. This makes it impossible(?) to ...
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33 views

Obtain Precision and Recall from Click through data

I am trying to build a graph of precision and recall using click data. I have two data sources. First data source has all the user clicked item_ids based on a given query_id. Second data source has ...
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37 views

Matrix Factorization in Recommender Systems: Multiple solutions?

I have implemented a recommender system for predicting user ratings based on the matrix factorization approach. $$ r_{ui}=μ+b_u+b_i+q_i^T p_u $$ Where q and p are found by mimization of the squared ...
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60 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 ...
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18 views

Diversity metric for recommender

I have some specific question regarded to diversity metric for recommender. Do I need real the test set to evaluate the diversity metric? I want to understand it basically. For recall and precision ...
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1answer
26 views

Sparse Collaborative Filtering

Does anyone know of any Python code examples for sparse collaborative filtering. Everything I can find revolves around using prebuilt packages (e.g. Mahout, GraphLab), but I'm learning to learn the ...
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35 views

Statistical quality criteria for classifiers and recommenders

I want to know several points regarding the evaluation of data sets. I would like to know which metrics are the best for the evaluation of: a) recommenders and classifiers b) online and offline ...
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1answer
80 views

Low Rank Matrix Factorization Collaborative Filtering - given a sparse set of feature data

I'm playing with a "minor" variation on an otherwise typical low rank matrix factorization collaborative filtering algorithm. I'm mostly following Andrew Ng's description in Coursera's online ML ...
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37 views

Recommendation Engine With Physical Distance Cutoff

I am looking to develop a recommendation engine for local stores to users. There are approximately 1 million stores in the database and around 1 million users. The 1Mx1M matrix for a user-based ...
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1answer
108 views

Recommenderlab - Getting the user_id out the RealRatingMatrix containing UBCF recommendations

I'm trying to use recommenderlab (with RSTUDIO) to get recommendations.When I'm using UBCF I can't extract the user id out of the realRatingMatrix containing the predictions, although I can do it with ...
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31 views

Inference on survey results

I have user responses to different types of surveys (e.g. "car survey", "lifestyle survey",...). Most of the users have answered just a very small number of surveys. I would like to predict the ...
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2answers
103 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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1answer
52 views

How to verify implementation of SVD in Javascript

I have implemented the SVD algortihm for my Node.js project for collaborative filtering of a sparse dataset based on this paper by GroupLens. For calculating the SVD, I am using the package node-svd ...
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36 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 ...
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85 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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34 views

Examples for Attacks on Recommender Systems [closed]

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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34 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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133 views

Incremental SVD Recommendation System

I have the following Octave/Matlab code to compute an SVD-like matrix-decomposition: ...
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13 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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36 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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28 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
71 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
35 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
28 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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55 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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44 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
70 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: $G = U \Lambda U^T$ ...
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1answer
70 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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70 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
26 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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2answers
87 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
75 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 ...
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143 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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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
129 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 ...
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2answers
173 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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86 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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48 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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102 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 ...