Questions tagged [recommender-system]

Everything related to recommender systems

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32
votes
4answers
16k 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 ...
19
votes
3answers
286 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?
17
votes
2answers
8k 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 ...
15
votes
2answers
28k 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
4answers
15k 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?
12
votes
2answers
2k 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 ...
12
votes
1answer
4k 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: ...
11
votes
3answers
7k 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. ...
11
votes
3answers
6k 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
2answers
6k 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 ...
10
votes
1answer
8k 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 ...
10
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 ...
10
votes
1answer
9k 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 ...
9
votes
2answers
3k 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 ...
9
votes
3answers
659 views

Binary (Unary) Recommendation System with Biased Views

I would like to create a content recommendation system based on binary click data that also takes views into account. What content a user has been exposed to, and therefore has the chance to click ...
9
votes
3answers
236 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
936 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
2answers
3k 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 ...
8
votes
3answers
476 views

Why is deep learning used in recommender systems?

I am currently reading a lot about recommender systems (RS) and came across that many RS are based on deep learning. However, I never find a good scientific article why deep learning is used in RS and ...
8
votes
1answer
2k views

How is the cross-product transformation defined for binary features?

I am reading the paper on Wide & Deep learning and for the wide component, it states that one of the most important transformations is the cross-product transformation. This is defined as follows: ...
8
votes
1answer
232 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 ...
8
votes
2answers
771 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
6k 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 ...
6
votes
4answers
205 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 ...
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
8k 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
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
158 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
582 views

Understanding the softmax output in Youtube's recommender

This question has been asked before, but never (that I can see) satisfactorily answered. I'm reading Youtube's paper on their recommender system. The system has two elements, the first of which is a ...
6
votes
1answer
2k 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
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 ...
6
votes
1answer
556 views

Understanding Youtube recommender (candidate generation step)

I'm trying to understand Deep Neural Networks for YouTube Recommendations. Their candidate generation step outputs top N items via softmax (with negative sampling) at training time . via ...
5
votes
2answers
4k 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 ...
5
votes
2answers
7k 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 ...
5
votes
3answers
998 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
884 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
700 views

How to use ndcg metric for binary relevance

I am working on a ranking problem to predict the right single document based on the user query and use the NDCG metric to measure the model. Given the details : Queries ( Q ), Result Document ( D ),...
5
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 ? ...
5
votes
1answer
87 views

Evaluating the performance of a machine learned recommendation system

I have a set of resumes $R=\{{r_1,...,r_n\}}$, which I've transformed to a vector space using TF-IDF. Each resume has a label, which is the name of their current employer. Each of these labels comes ...
5
votes
2answers
939 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 ...
5
votes
1answer
174 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
2answers
586 views

What is difference between Nearest Neighbor and KNN?

I was taking the tutorial of making Recommendation system , there I read that Nearest Neighbor is different from KNN classifier . Could anyone explain that what is Nearest Neighbor and how it is ...
4
votes
4answers
803 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 ...
4
votes
6answers
466 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
453 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
199 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
5answers
6k views

Machine learning algorithm for ranking

I am working on a ranking question, recommending k out of m items to the users. The evaluation metric is average precision at K. Both R and Python have xgboost can be used for pairwise comparison ...
4
votes
2answers
267 views

How to use hashing trick with field-aware factorization machines

Field-aware factorization machines (FFM) have proved to be useful in click-through rate prediction tasks. One of their strengths comes from the hashing trick (feature hashing). When one uses hashing ...
4
votes
1answer
502 views

TS-SS and Cosine similarity among text documents using TF-IDF in Python

A common way of calculating the cosine similarity between text based documents is to calculate tf-idf and then calculating the linear kernel of the tf-idf matrix. TF-IDF matrix is calculated using ...
4
votes
2answers
267 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 (...

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