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0
votes
0answers
84 views

Adding content information to matrix factorization-based recommender

I'm currently using a matrix factorization method to generate recommendations (for info on this, check: Matrix Factorization Techniques for Recommender Systems). At the moment, my rating estimate is ...
0
votes
0answers
44 views

alternating least squares and all zero rows-columns

I'm trying to implement the algorithm described in Large-scale parallel collaborative filtering for the Netflix prize and I'm having some trouble understanding how to deal with a rating matrix that's ...
1
vote
1answer
40 views

When should I update a recommendation engine?

[I asked this on StackOverflow and was told it would be a better fit here] I am including a basic recommendation engine in a very small project for my final exam. I understand the code and the math ...
0
votes
0answers
30 views

Inferring user rating from play counts

I am interested in converting user play counts in some sort of rating. Building an artist recommendation system and I have access to user play counts. I can't use them directly in a user-based ...
2
votes
0answers
124 views

Matrix factorization and gradient descent for recommender systems; user bias?

I've been reading about using Matrix Factorization techniques to do collaborative filtering. A popular thing to do seems to be to add user and item biases into the ratings prediction. What I don't ...
1
vote
0answers
32 views

MAE/MSE with or without square root

I read some papers about recommender systems and information retrieval, where Mean Absolut Error and Mean Squared Error are mentioned. But I've found some differences between the formal definition of ...
2
votes
1answer
204 views

Precision - Recall: Graphical Representation

I'm a little bit confused with precision recall. I read some papers about recommender systems, where in the one paper they have a graphical representation and in the other papers they don't (they just ...
1
vote
1answer
51 views

Calculate boundary for MAE given RMSE

E.g. from the Netflix prize I know that the best RMSE = 0.8563 where the test dataset has a size of n=1,408,789. Can I calculate a boundary for the MAE. If not, why can't I calculate a boundary? I ...
1
vote
0answers
56 views

recommender system implicit rating to ordinal scale

There are 4 ways a user can show preference for an article within my news app: number of times an article was viewed; for how long was the article viewed; whether it was favorited or not; and number ...
2
votes
1answer
45 views

Using collaborative filtering to “clean” data and the other way around

I am considering two types of systems - which might have more appropriate names: Recommender systems: These recommender systems are based on collaborative filtering methods, both model-based and ...
3
votes
1answer
82 views

Most relevant algorithms for Collaborative Filtering to test against

I am working on algorithms for collaborative filtering (CF). As part of this work, I want to compare a new algorithm to previous approaches to the problem. I am also surveying the most important ...
2
votes
0answers
50 views

Appropriate threshold to map a similarity value to an edge in a graph

In order to cluster users given a user-item binary matrix data, I am planning to first find user's similarity (Jaccard) and then use graph theory to isolate clusters (communities). I need to map the ...
1
vote
0answers
33 views

improving user preferences based on behavior

I have a project to improve user experience in our software by gathering data on their actions while using the software and comparing that to preferences they made when initializing the software. The ...
1
vote
0answers
90 views

Matrix factorization vs random walk with restart for recommender systems

Suppose I want to handle "friend recommendation" problem on a large social network graph. I came across random-walk-with-restart as one technique used. I was thinking of using matrix factorization as ...
1
vote
1answer
75 views

Hierarchical recommender

I have a hierarchical feature matrix, By that I mean that each item may belongs to one or more category, so my data will look something like that |  User |  Categ  | Item  | ...
2
votes
0answers
51 views

Collaborative filtering through matrix factorization with logistic loss function

Consider collaborative filtering problem. We have matrix $M$ of size #users * #items. $M_{i,j} = 1$ if user i likes item j, $M_{i,j} = 0$ if user i dislikes item j and $M_{i,j}=?$ if there is no data ...
1
vote
0answers
43 views

Sparse estimated data recommender system

Premise: For a product and a user, the system has to recommend him/her other non-users related with him/her that are most likely to be interested in that same product. Available data includes: ...
1
vote
1answer
94 views

Mahout Scability

Do you know any real world examples of how much Mahout can scale? I wonder how much it can scale in collaborative filtering, clustering, and classification ?
1
vote
0answers
102 views

Article recommendation dataset [closed]

I am experimenting with a recommendation systems for articles (news, magazines etc). Is there any dataset available for the same which includes the demographic information of users as well? I am ...
0
votes
0answers
183 views

Dataset for article recommendation with demographic data

I am experimenting with a recommendation systems for articles (news, magazines etc). Is there any dataset available for the same which includes the demographic information of users as well? I am ...
1
vote
0answers
85 views

Collaborative filtering and implicit ratings; normalization?

I want to use the time a user spends viewing an article as an implicit rating of how much the user likes the article. My question is how do I normalize this information across all users. At the ...
3
votes
2answers
104 views

Prediction and explanation of user rating based on multiple criteria

I'm trying to figure out a way to both predict how a user would rate a certain document, as well as an explanation of why certain documents are rated a certain way. A user is represented by: ...
2
votes
0answers
40 views

Predicting user selections based on similar user

Lets say you give a set of users a set of polls, or give them a choice of foods to eat, or let them listen to a group of songs (guess like pandora). So looking at the choices that all the users make ...
0
votes
0answers
180 views

Are there any tagging dataset for evaluating a recommender system? e.g. user--tag--item--date [closed]

I need datasets that has at 4 data elements (user, tag, item, date) to test my recommender system, in which date is when the user add one or many tags to the item. The user and item can be provided as ...
4
votes
2answers
378 views

What happens when you apply SVD to a collaborative filtering problem? What is the difference between the two?

In Collaborative filtering, we have values that are not filled in. Suppose a user did not watch a movie then we have to put an 'na' in there. If I am going to take an SVD of this matrix, then I have ...
6
votes
1answer
344 views

SVD of a matrix with missing values

Suppose I have a Netflix-style recommendation matrix, and I want to build a model that predicts potential future movie ratings for a given user. Using Simon Funk's approach, one would use stochastic ...
2
votes
1answer
136 views

AUC vs error rate for classification

I'm trying to build a recommendation system, and have a bunch of (item,item_features,liked) triplets, where liked is binary. Most items are not liked. So I'm running a logistic regression with ...
5
votes
2answers
866 views

How do I use the SVD in collaborative filtering?

I'm a bit confused with how the SVD is used in collaborative filtering. Suppose I have a social graph, and I build an adjacency matrix from the edges, then take an SVD (let's forget about ...
1
vote
1answer
66 views

Clustering groups based on frequency and last date of choice

I have data coming in the following format: UserId, GroupId, Frequency (how many times the user chose the group), Max timestamp (the last time the user chose the group). Based on this dataset we need ...
3
votes
1answer
178 views

Evaluating recommender systems with (implicit) binary ratings only

I'm analyzing a set of news articles and user libraries. User library is the set of news articles shared by one user. Obviously, the rating is 1 (the article is in user's library) and 0, otherwise. I ...
3
votes
1answer
84 views

What is a distance or similarity metric that takes into account the improbability of a match?

Suppose I want to measure similarity between users. If two users match on an item that is very improbable, I want to give greater weight to that.
3
votes
1answer
266 views

Does it make sense to measure recall in recommender systems?

Assume I've built a recommender system that (given say movie rankings or whatever of many users) will produce a list of 10 recommended movies for each user to watch. Imagine that I also have some ...
2
votes
0answers
77 views

Dynamic recommender systems [duplicate]

Possible Duplicate: Dynamic recommender systems A Recommender System would measure the correlation between ratings of different users and yield recommendations for a given user about the ...
8
votes
4answers
161 views

Dynamic recommender systems

A Recommender System would measure the correlation between ratings of different users and yield recommendations for a given user about the items which may be of interest to him. However, tastes ...
3
votes
3answers
200 views

How to create recommender system that integrates both collaborative filtering and content features?

I am creating a Recommender System and want to incorporate both the ratings of "similar" users and the features of the items. The output is a predicted rating [0-1].I am considering a Neural Network ...
6
votes
5answers
411 views

Recommendation for a book about recommender systems

Can you recommend a book with good information that can be applied to developing a recommender system?
3
votes
1answer
78 views

How to deal with information of variable length?

This is the question arised from my previous question. Basically, I am trying to build a prediction model for movie's rating. So I have to deal with numeric attribute such as ...
1
vote
0answers
95 views

Evaluation methods for personalized recommendation

I am currently trying to verify and evaluate a personalized recommender system I am working on, which seems like a huge task. Evaluating a static recommender system is rather easy and can be done with ...
0
votes
0answers
349 views

Model-based learning algorithm for recommendation engine

Can you please suggest me a good model-based learning algorithm to recommend items to the user? Is there any open source implementation available on model based learning algorithm? I am sure Apache ...
2
votes
2answers
307 views

Algorithm to calculate difference in users' tastes

I have data like Person $A$ like movies ['X','Y', 'Z'] and he dislikes ['V']. Person $B$ like movies ...
3
votes
1answer
170 views

Suggestions for embedded interactive analytical functionalities?

We're thinking of adding an interactive near real-time analytics functionality (a-la "Google Analytics") to a product Movie Recommender Engine. We need to let the user interactively create analyses ...