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.

learn more… | top users | synonyms

0
votes
0answers
3 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 ...
0
votes
0answers
6 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 ...
0
votes
0answers
6 views

How can I find a dataset in the field of eye tracking for a recommender system? [on hold]

I want to implementation a web recommendation system by combining Markov and eye tracking, but I can not find a dataset that can be used to do!
0
votes
2answers
29 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 ...
0
votes
0answers
11 views

Matrix factorization: Item vector clustering.

I tried to run k-means clustering (with euclidean distance) on top of item vectors that come from a matrix factorization algorithm. The results make absolutely no sense. Most (95%) items are in the ...
0
votes
0answers
13 views

Recommender; Matrix factorization: How to compute item to item recommendations

I am trying to compute item to item recommendations. Basically to output related/similar movies. Not depending on the user but only on the movie. My first thought was to compute the dot product for ...
0
votes
0answers
25 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 ...
0
votes
0answers
45 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? ...
0
votes
0answers
11 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 ...
12
votes
4answers
628 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 ...
0
votes
0answers
11 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)?
1
vote
1answer
66 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 ...
2
votes
1answer
79 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 ...
0
votes
0answers
37 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 ...
0
votes
0answers
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 ...
0
votes
1answer
54 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 ...
0
votes
0answers
44 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 ...
0
votes
0answers
27 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 ...
0
votes
0answers
9 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 ...
1
vote
1answer
54 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' ...
1
vote
0answers
16 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 ...
0
votes
0answers
9 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 ...
2
votes
0answers
76 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
votes
1answer
45 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 ...
0
votes
0answers
25 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 ...
1
vote
0answers
54 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
votes
1answer
97 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) ...
0
votes
0answers
26 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, ...
4
votes
3answers
112 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 ...
3
votes
0answers
58 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: ...
0
votes
1answer
38 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 ...
0
votes
1answer
50 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 ...
1
vote
0answers
449 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 ...
1
vote
1answer
81 views

Recommending products to user without rating

I have a data set which consists of users and products purchased by them. But, I don't have any ratings for each of the purchased products. I am trying to use item-item collaborative filtering ...
0
votes
1answer
124 views

Collaborative Filtering for Implicit Feedback Datasets

I'm building a recommendation engine using ALS, as described in Hu/Koren/Volinsky http://labs.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf I'm confused about a few points: How should one interpret ...
1
vote
1answer
113 views

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 ...
0
votes
0answers
14 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 ...
0
votes
0answers
75 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 ...
1
vote
0answers
57 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 ...
1
vote
0answers
64 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 ...
0
votes
0answers
115 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 ...
1
vote
0answers
88 views

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 ...
1
vote
0answers
191 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 ...
0
votes
0answers
22 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 ...
3
votes
1answer
133 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 ...
3
votes
1answer
94 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 ...
2
votes
1answer
86 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 ...
0
votes
1answer
159 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 ...
2
votes
1answer
192 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 ...
0
votes
1answer
75 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 ...