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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27 views

Matrix Factorization in Recommender Systems: Uniqueness of SVD?

I was studying the collaborative filtering approach about recommender system and I read about matrix factorization approach. In SVD version, I have not figured out how the non-uniqueness of the ...
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10 views

Mahout Item Recommender score for every combination of user and item

We're looking to create a recommender using Mahout that will generate a recommendation score for every combination of user and item based on a users rating of items. The number of items is fairly ...
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1answer
19 views

Recommenderlab not working with highly sparse binary data?

The data used is a ratings matrix generated from simple 0-1 yes/no click data based on whether or not a user visited a section of a website. This is implicit voting since if a user is interested in a ...
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14 views

Recomender System algorithm for Pinterest-like system

I want to build recommender system for following setup: I’ve got users U Each user U(i) has set of features Fu. Your most common ones – gender, age, country, interests etc. I’ve got collection of ...
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1answer
33 views

Evaluate a supermarket recommender system

I have finally come up with a recommender system for the supermarket which now suggests products to users based on implicit collaborative filtering. But I am stuck at a point where I do not know how ...
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1answer
32 views

Get distance matrix directly condensed

I am developing a content-recommender Python system and most of my items (~8 millions) are static so I have thought about pre-computing the top 150 similar items for each item. This way, when a user ...
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1answer
37 views

Build a Recommender System for a List of Items

So, I am having troubles picturing how to do this. I want to build a recommender system, but rather than doing it on a user/item based recommendations I want to do a list_of_items/item based ...
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41 views

What are the best methods for combining two recommendation model scores into one final score?

For each user, items are ranked based on scores which are coming from two algorithms using different data. This results in very different recommendations, some items are recommended basis first model ...
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20 views

Help with understanding how the recommendation works

I am trying to build a recommender system for a super market and do not really have any ratings for any product apart from the number of purchase of each item. Sample of the data is like: ...
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27 views

Approach to find similarity between two different types of matrices

I'm using the movielens dataset to give recommendations to a user based on genre of the movies. I have two matrices, one contains the genre the user likes. We are considering user3 for now I also ...
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14 views

Finding cross-disciplinary themes in course listings

I’m building a dataset of course listings and am looking for methods/ algorithms to reveal clusters, connections, cross-disciplinary themes and threads that could be found in different courses. ...
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38 views

Stochastic content-filtering recommender w/o rating (eg Pandora)

I want to build a content-based filtering recommender akin Pandora's, where the user likes or dislikes an item. No star-rating (just yes/no), no collaborative filtering, hard-coded features, one item ...
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7 views

How can I add one continue variable without computing its interaction using factorization machines?

I have an input data which consist of 1) User, 2) Item, and 3) a continuous variable. It is a regression problem. So the input can be as below: ...
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96 views

Cosine similarity calculation in recommenderlab package in R

I am trying to calculate the cosine similarity between a pair of users using recommenderlab and an online cosine similarity calculator. Below is the sample data used for computations: ...
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1answer
24 views

Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf ...
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19 views

Recommender system - combining collaborative filtering and content based

I read a lot of stuffs about combining collaborative filtering and content-based but no one seems to bother explaining how to get those so-called hybrid recommenders or they do it so shallowly that I ...
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1answer
44 views

Recommender System + Collaborative filtering without users

So I have a problem where I have a dataset that includes a list of Tools that are tied to Tasks. The data is structured as follows: The users do not rate the Tools, they simply use them in a method ...
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26 views

Is item based collaborative filtering part of model based approach - recommendation systems?

I am going through the documentation on recommenderlab package in R. I have few questions on how the user/item based collaborative filtering can be considered as a part of model based approaches. ...
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2answers
77 views

Probability of a product being bought given a seed product (recommender system)

I'm building an e-commerce recommender system. Given a seed (bought) product I want to recommend a product that has the highest probability of being bought. I model this as a conditional probability ...
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1answer
50 views

Dimensionality reduction for high dimensional sparse data before clustering or spherical k-means?

I am trying to build my first recommender system where i create a user feature space and then cluster them into different groups. Then for the recommendation to work for a particular user , first i ...
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32 views

Spark ALS-WR giving the same recommended items for all users

We are trying to build a recommendation system for a supermarket with diverse item types (ranging from fast-moving grocery to low-moving electronic items). Some items are purchased more frequently in ...
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11 views

Adjustable Recommendation System

I've never built a recommendation system before but I understand the concepts behind a collaborative filter. Given a set of implicit feedback (e.g., students who get above a B on a course vs students ...
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2answers
202 views

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

Is it possible to use advance optimisation(L-BFGS, Conjugate gradient) for an collaborative filtering systems vs just using gradient decent? I ask this because of the need to calculate both X and ...
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78 views

Recommendation System in Python - weighted average with negative factors

I am building a recommendation system in python on the 100k movielens dataset. My code build a movie/user matrix where the (x,y) element is the rate that the user y gave to the movie x. Since i want ...
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50 views

How to recommend items based on historical shopping basket contents

Say you know the contents of shopping baskets many clients had at checkout. Based on this, you want to recommend an item to buy based on the item currently in the basket. How would you do this? ...
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13 views

Recommender Systems with Multiple Correlated Values as Outputs

I'm trying to apply the matrix factorization algorithms to a problem where the output matrix does not have scalar values (as in recommendations) but rather, the outputs are vectors and the ...
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4answers
665 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 ...
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14 views

How to use the Vector space model in a content based recommender

Does it make sense to use Vector space model for a content based recommendation with users and items described using a set of Simple Tags 0/1 (keyword applies or doesn’t)?
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1answer
99 views

Apriori versus Jaccard Coeff for recommender-system

I developed a market basket program for a retailer. I used the pymining library association rules function. I assume this uses the Apriori algorithm. I got 340,000 rules for an input of 17,000 ...
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1answer
117 views

Standard library for Funk SVD or other gradient descent SVD/eigenvalue

I want to get the first few eigenvectors of real symmetric matrices with missing values. Since it has missing values, I won't be able to use the common linear programming techniques, but stochastic ...
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46 views

Collaborative filter recommends same products to all users

I'm building a collaborative filter using matrix factorization and alternating least squares. For some reason, the math always give me back the same recommendations for all my test users (not always ...
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10 views

Features & Models to compute the probability of certain customer accepting an offer/product from a bank?

What are the features & models that can be used to compute the probability of a certain customer accepting an offer/product from a bank? After some research, I came to know of what is called ...
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1answer
88 views

Neural Networks for Content Based Recommendation system

I'm working on the TED Dataset which has the transcript of each TED Talk. I have around 1000 such TED talk transcripts and I need to recommend 3 TED Talks based on the Transcript of these talks. As ...
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48 views

EdgeRank for eCommerce product feed

I am running my eCommerce store with around 1000 products with 10 categories. I want to show these products in feeds. But there are lots of products so its very complex to define priority list for ...
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31 views

Using co-occurrences as (Bayesian?) recommendation system

My question is motivated / inspired by this blog post, where it is shown that a vendor with 90 positive reviews and 10 negative is preferable to one that has only 2 positive and 0 negative. Assume I ...
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10 views

Recommendations based on Averages

I have a website where people can rate objects. Each objects has a number of properties. Objects do not all have the same number of properties, it could have anywhere from 1 or greater. All the ...
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1answer
56 views

Statistical significance of item-to-item relationship

Context: I have an e-commerce application - so I have users and products. I'm trying to build an item-to-item recommendation system based upon user behavior. In particular I'm taking all the users' ...
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17 views

Similarity between an item and a set

I have two items $I_0$ and $I_1$ and a set $S$. What I want to achieve is to compare which item (either $I_0$ or $I_1$) is more similar to the set. I can compare both $I_0$ and $I_1$ to every element ...
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13 views

Problem with very sparse, large purchase event dataset for recommender methods

I have a dataset purchase events by customers, and it is something like 2 million events(20000 products, 450000 customers), with 99.98% sparsity and I want to process it to get new recommendations for ...
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0answers
114 views

Cosine similarity between two types of user profiles

I have a movie dataset containing preference (ratings) of users on movies and also attributes of movies (genre, cast, director). I created two types of profiles for users based on the movie attributes ...
2
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1answer
70 views

Collaborative Filtering: How to update user vectors online?

As the subject says: How would one update user vectors online while having static item vectors? Run the learning step only on the user vectors? But that would still take too much time to be ...
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27 views

Are neighborhood models mathematically equivalent to latent factor models in collaborative filtering?

This is a counter intuitive question, since most papers consider neighborhood models as different from latent factor models. However, I'm pretty sure I've read a research paper which says they are ...
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0answers
105 views

What's a good evaluation method for recommendation systems?

I'm using the 100k MovieLense dataset to build a recommendation system in R, using the recommenderlabpackage. From what I understood, the ...
2
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1answer
159 views

Overfitting in Matrix Factorization models used in Recommender Systems (Collaborative Filtering)

I'm wondering if I should check if a Matrix Factorization model I built for recommendation by Collaborative Filtering is overfitting. I trained a model using MLlib ALS (Alternating Least Squares) ...
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0answers
27 views

Collaborative filtering when the ratings are estimates

I have a matrix (User X Item) of binomial estimates of click through rates. That is, each entry in the matrix is the estimate of the probability that a specific user will click on a specific target, ...
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3answers
134 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 ...
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0answers
64 views

How to model and predict a user's preference by their click-through history?

I am trying to model my users' food preferences so that I can recommend restaurants which he/she might be satisfied. The following is some sample data: ...
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1answer
52 views

Understanding Item-based collaborative filtering

I try to understand item based collaborative filtering by studying the recommenderlab documentation. On page 7 the calculation of expected ratings for items unrated by the user is very nicely ...
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1answer
57 views

Model based approaches to content based recommenders. How does this work?

I have a question regarding the use of model based approaches to recommender systems. So, the goal is to create a model that predicts the user reaction to a specific item. Either a rating scale or ...
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503 views

CV (Curriculum Vitae) Recommender system - guidance

Please note that I am a total beginner with machine learning and artificial intelligence and also a novice with Python (I'm sure I have a very non-Pythonic way of writing code). I have a college ...