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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Calculating Cosine Similarity with Matrix Decomposition (matrix multiplication with normalized columns)

To calculate the column cosine similarity of $\mathbf{R} \in \mathbb{R}^{m \times n}$, $\mathbf{R}$ is normalized by Norm2 of their columns, then the cosine similarity is calculated as ...
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10 views

How should I measure difference between user behavior / model performance on different population?

I am developing a recommendation engine whose goal is to suggest data exploration routes to non-technical users. The underlying model is content based, with the training data made up of the behavior ...
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19 views

Precision and Recall for recommendation system

I am working on movielens 100K movie data for recommendation system. I divide the data into test and training and calculate the precision and recall. In testing there are more than 10K users chosen ...
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33 views

Interpretation of matrix factorization results

Matrix factorization methods are known to give good results pertaining to problems like movie recommendation. The method reduces the feature space, which is then used for recommendations. For example ...
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37 views

Why RMSE is too small?

Sorry, I'm a newbie at recommender systems, but I wrote few lines of code using the Apache Mahout library. My dataset is pretty small, 500x100 with 8102 cells known. The dataset is actually a ...
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35 views

Non-negative matrix factorization in recommender systems

As i understand, in NMF we should have our three matrices elements non-negative. But i can't understand how to do it so far. Shouldn't we just initialize our factor matrices at the start with random ...
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What are some applications of the Apriori algorithm?

My understanding is that for online promotions the Apriori algorithm is not used as much as say collaborative filtering algorithms largely because the cost of marketing to the long tail is so cheap ...
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46 views

Alternating Least Squares (ALS) - Why two different kinds of setting for $\lambda$ and $\alpha$?

I am trying to use Spark ALS to do recommendation with implicit feedback. However, I found there are two totally different kinds of settings available: The first one is the setting used by the ...
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18 views

Validation tool for recommendation system

I have developed a recommendation system which recommends the products based on the transaction history of the customer. All I have is one year of transaction data with no information on purchase ...
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82 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 ...
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51 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 ...
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77 views

Collaborative filtering using a linear model

Consider I have a set of movies and a set of users ($A$,$B$,$C$,$D$) and a matrix with related scores (I can have gaps in this matrix). Consider a linear regression model where a specific user A's ...
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51 views

How to divide dataset into training and test set in Recommender Systems?

I am working on two simple recommender systems - Collaborative (item-item a user-user) and Content Based. I would like to evaluate prediction accuracy of these systems. I am used to divide dataset ...
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69 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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45 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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20 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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29 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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18 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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46 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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25 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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56 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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45 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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47 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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25 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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55 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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37 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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66 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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142 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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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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54 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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38 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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116 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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57 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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36 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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246 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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103 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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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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103 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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37 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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60 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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186 views

Incremental SVD Recommendation System

I have the following Octave/Matlab code to compute an SVD-like matrix-decomposition: ...
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16 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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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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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
105 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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85 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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30 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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97 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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92 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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81 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?