Questions tagged [recommender-system]

Everything related to recommender systems

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23
votes
4answers
9k views

Meaning of latent features?

I am learning about matrix factorization for recommender systems and I am seeing the term latent features occurring too frequently but I am unable to understand ...
18
votes
3answers
242 views

Does click frequency account for relevance?

While building a rank, say for a search engine, or a recommendation system, is it valid to rely on click frequency to determine the relevance of an entry?
16
votes
2answers
7k views

Recommending movies with additional features using collaborative filtering

I am trying to build a recommendation system using collaborative filtering. I have the usual [user, movie, rating] information. I would like to incorporate an ...
14
votes
2answers
25k views

Item based and user based recommendation difference in Mahout

I would like to know how exactly mahout user based and item based recommendation differ from each other. It defines that User-based: Recommend items by finding similar users. This is often harder to ...
12
votes
2answers
1k views

Preference Matching Algorithm

There's this side project I'm working on where I need to structure a solution to the following problem. I have two groups of people (clients). Group A intends to ...
11
votes
3answers
5k views

Field Aware Factorization Machines

Can anyone explain how field-aware factorization machines (FFM) compare to standard Factorization Machines (FM)? Standard: http://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf "Field Aware": ...
10
votes
1answer
3k views

Spark ALS: recommending for new users

The question How do I predict the rating for a new user in an ALS model trained in Spark? (New = not seen during training time) The problem I'm following the official Spark ALS tutorial here: ...
9
votes
2answers
4k views

Benchmark datasets for collaborative filtering

I'd like to test a new algorithm for collaborative filtering. A typical use case is to recommend movies based on the preferences of users similar to the specific user. What are some common benchmark ...
9
votes
1answer
1k views

How should one deal with implicit data in recommendation

A recommendation system keeps a log of what recommendations have been made to a particular user and whether that user accepts the recommendation. It's like ...
9
votes
3answers
196 views

What recommendation engine for a situation where users can only see a fraction of all items?

I want to add a recommendation feature to a document management system. It is a server on which most company documents are stored. Employees browse the web interface and click to download (or read ...
9
votes
2answers
841 views

How to model user's buying behavior on Amazon?

For our final course project in Data Science, we proposed the following- Give the Amazon Reviews Dataset, we plan to come up with an algorithm (thats roughly based on Personalized PageRank) that ...
8
votes
1answer
5k views

How to split train/test in recommender systems

I am working with the MovieLens10M dataset, predicting user ratings. If I want to fairly evaluate my algorithm, how should I split my training v. test data? By default, I believe the data is split ...
8
votes
2answers
251 views

What should be the value of non-rated field when finding cosine similarity

I am working on a very basic book recommender system. I want to know what to do with the fields which aren't rated by the user when finding cosine similarity, should we ignore them and calculate only ...
7
votes
2answers
4k views

Recommender system based on purchase history, not ratings

I'm exploring options for recommender systems optimized for the insurance industry, which would take into account i) product holdings ii) user characteristics (segment, age, affluence, etc.). I ...
7
votes
1answer
5k views

How do you calculate how dense or sparse a dataset is?

I'm looking deeper into collaborative filtering. One really interesting paper is "A Comparative Study of Collaborative Filtering Algorithms" http://arxiv.org/pdf/1205.3193.pdf In order to select ...
7
votes
2answers
3k views

Which supervised learning algorithms are available for matching?

I'm working on a non-profit where we try to help potential university applicants by matching them with alumni that want to share their experience/wisdom and, at the moment, it is happening manually. ...
6
votes
2answers
2k views

Create most “average” cosine similarity observation

For a recommendation system I'm using cosine similarity to compute similarities between items. However, for items with small amounts of data I'd like to bin them under a general "average" category (in ...
6
votes
2answers
1k views

Do recommendation systems necessarily use machine learning algorithms?

I am studying about evaluation of both recommendation systems and machine learning algorithms in recent times, trying to define a scope for my masters research. After some reading time I'm starting to ...
6
votes
2answers
7k views

Cosine Similarity for Ratings Recommendations? Why use it?

Lets say I have a database of users who rate different products on a scale of 1-5. Our recommendation engine recommends products to users based on the preferences of other users who are highly similar....
6
votes
2answers
2k views

Difference between using RMSE and nDCG to evaluate Recommender Systems

What kind of error measures do RMSE and nDCG give while evaluating a recommender system, and how do I know when to use one over the other? If you could give an example of when to use each, that would ...
6
votes
1answer
3k views

Bechmark for Movielens

I'm looking for a place to find benchmarks against which to evaluate performance on public datasets. In this instance, I'm interested in results on the MovieLens10M dataset. It seems to be ...
6
votes
3answers
116 views

Can we quantify how position within search results is related to click-through probability?

Suppose, for example, that the first search result on a page of Google search results is swapped with the second result. How much would this change the click-through probabilities of the two results? ...
6
votes
1answer
1k views

How to normalize results of Singular Value Decomposition (SVD) between 0 and 1?

I'm building a recommender system and using SVD as one of the preprocessing techniques. However, I want to normalize all my preprocessed data between 0 and 1 because all of my similarity measures (...
6
votes
1answer
182 views

Evaluating Recommendation engines

What is the standard way for evaluating and comparing different algorithms while developing recommendation system? Whether we need to have a predetermined annotated ranked dataset and then compare ...
5
votes
4answers
152 views

Business exception reporting

I work in an analytical role at a a large financial services firm. We do a ton of daily reporting over metrics that rarely change in a meaningful way from day to day. From this daily reporting, our ...
5
votes
3answers
799 views

Recommendation - item based vs user based [closed]

I have one clarification - First the definitions- User-based: Recommend items by finding similar users. This is often harder to scale because of the dynamic nature of users. Item-based: Calculate ...
5
votes
2answers
5k views

Can I use cosine similarity as a distance metric in a KNN algorithm

Most discussions of KNN mention Euclidean,Manhattan and Hamming distances, but they dont mention cosine similarity metric. Is there a reason for this?
5
votes
2answers
780 views

Item based recommender using SVD

I have an item-item similarity matrix. e.g. (the matrix is symmetric, and much bigger): ...
5
votes
1answer
4k views

Item-Item similarity based on text

We're build an item-item recommender based on the text descriptions of the items. Our initial approach was to calculate the TF-IDF vectors for each item. We used a hashing tf with 5000 possible hashes ...
5
votes
1answer
159 views

Data scheduling for recommender

I do at the moment some data experiments with the Graphlab toolkit. I have at the first next SFrame, with the three columns: Users Items Rating The pair in the ...
4
votes
6answers
158 views

Distance between users

I want to compute the "distance" between users in order to return the top n similar users, for any given user. For each user a have a bunch of features. This is close to a recommendation system, ...
4
votes
2answers
290 views

Factorization Machine - prevent over fitting

I was recently asked this question in an interview and wondered what the answer would be - "How do Factorization Machines get around the overfitting problem when using second-order interactions?"
4
votes
2answers
3k views

SVD for recommendation engine

I'm trying to build a toy recommendation engine to wrap my mind around Singular Value Decomposition (SVD). I've read enough content to understand the motivations and intuition behind the actual ...
4
votes
2answers
5k views

Item Based Collaborative Filtering with No Ratings

I am building a recommender for web pages. For each web page in our data set, we wish to generate a list of web pages that other users have also visited. Our data only shows that a user has either ...
4
votes
2answers
146 views

Price optimization for tiered and seasonal products

Assuming I can collect the demand of the purchase of a certain product that are of different market tiers. Example: Product A is low end goods. Product B is another low end goods. Product C and D are ...
4
votes
1answer
2k views

Mahout Similarity algorithm comparison

Which of the following is best (or widely used) for calculating item-item similarity measure in mahout and why ? ...
4
votes
1answer
163 views

Recommendation/personalization algorithm conflict

I'm trying to build a recommendation engine for an e-commerce site. By using the common recommendation approach, I'm assuming that each product I recommend has the same value, so all I need to do is ...
4
votes
1answer
911 views

Recommendations and Missing Data in Deep Learning

In this research paper, it is discussed how to combine deep learning with wide (shallow) learning to achieve both generalisation and the ability to learn correlation/association rules. The input ...
4
votes
3answers
84 views

Make use of relationships on recommendation systems

I have a data set of user rating for movie as user_name, product_name, user_rating and I am using this data to recommend new movie to user (collaborative ...
4
votes
1answer
125 views

Recommender system for next career step

I want to build a recommender system that suggests the next step in your career. About the dataset. About 50'000 Users with following informations: Skills (tags, string value) every job they did (...
4
votes
1answer
178 views

Graph-Document-Recommendations

I want to build a Recommendation System to recommend products to users. This is for research purposes. The context-system the engine will be integrated in is also not build yet. So right now I am ...
4
votes
1answer
615 views

How to deal with a sparse matrix when using a perceptron based recommender system?

I'm constrained to use a perceptron based method. I have a user-item matrix filled with rating data on scale of 1 to 5 like this, with around 50% of the matrix with no data: ...
4
votes
1answer
235 views

How to deal with position bias in search?

In search, position of the search result affects the click-through rate a great deal. How do people usually deal with this ? In practice how to remove such bias to create unbiased training data for ...
3
votes
2answers
101 views

What kind of data is not appropriate using CF to do recommendation?

I am currently working on a recommendation system for daily news. At first, I evaluated all the recommender algorithms and their corresponding settings (e.g., similarities, factorizers, ...etc) ...
3
votes
4answers
246 views

How can conclusions be drawn from recommendation systems evaluation?

From my research, a recommendation system are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And basically exists many ...
3
votes
3answers
281 views

Item Similarity with Location Feature

I'm currently learning about Collaborative Learning and Content-based Recommendation. One of the main things that is discussed in both methods is about calculating similarity between two users or two ...
3
votes
1answer
285 views

Interpretation of an SVD for recommender systems

The idea is to motivate the SVD for use in a recommender system. Consider a matrix $A\in \mathbb{R}^{f\times u}$ where $A_{ij}$ caputures how user $j$ rates film $i$ (on a scale from 1-10, some ...
3
votes
3answers
728 views

Solution for in Time/Space Complexity challenge in Recommendation System?

I have a book Recommendation System project and have a huge data set of feature vectors. What is the best solution for in memory computation? I mean, the program should: calculate the cosine ...
3
votes
2answers
1k views

Recommendation engine with mahout

I have a list user data: user name, age, sex, address, location etc., and a set of product data: Product name, Cost, description etc. Now I would like to build a recommendation engine that will be ...
3
votes
2answers
341 views

Deep Learning for Recommender System

I read about Recursive Neural Networks that they can convert Documents to distributed word representation. In the context of new article recommendation, I am thinking to use this model to convert ...