Statistical Analysis Stack Exchange
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Questions tagged [recommender-system]

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.

A recommendation system, or recommendation engine, is an automatic tool that recommends goods to a specific user. There are many examples on online commercial websites: Netflix or IMDB recommends movies, Amazon recommends books any many more, etc. The input of such algorithms are most commonly the past purchases of the user, the rating the user gave to other goods, the items that have been purchased together ("customer who bought this also bought..."), the browsing history, etc.

See also Wikipedia.

Reference: adapted from Stack Overflow SE.

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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 change over time so old ratings might not reflect…
andreister
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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 (to start with). So, the inputs are a combination…
B Seven
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Finding similar items based on user likes and dislikes

I have data on content-based recommendations of movies and their attributes. Suppose a user likes x,y and z and also dislikes c and d movies. I want to predict movies that he will like based on his likes and dislikes. It is actually quite easy to…
Freshblood
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Any public available evaluation frameworks for recommender systems?

I would like to know if there are any evaluation frameworks of recommender systems which are capable of evaluating rating prediction and topN recommendation (Precision and recall etc.). Maybe I need to find them in recommender frameworks? If so are…
CyberPlayerOne
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How to set preferences for ALS implicit feedback in Collaborative Filtering?

I am trying to use Spark MLib ALS with implicit feedback for collaborative filtering. Input data has only two fields userId and productId. I have no product ratings, just info on what products users have bought, that's all. So to train ALS I…
zork
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Matrix factorization for recommendations vs Collaborative filtering

Some texts seem to list matrix factorization as a method for collaborative filtering, and more specifically categorize them as a "model-based approach" (e.g. here and here), while others seem to treat them differently (e.g. see here where the…
Josh
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How do recommender systems incorporate user characteristics?

I'm new to recommender systems, and I've been reading about how user-based collaborative filtering can group similar users together and (for example) use their ratings to suggest movies to other similar users. However, I'm having trouble…
Nathan Calverley
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Two Source Collaborative Filtering

This question is based on the now classical work: Collaborative filtering with implicit feedback. I'm mainly interested in finding references for the question below. Suppose we are building a recommendation system from a two source dataset. As a…
Alex R.
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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 considered online I guess. From Logistic Matrix…
MachineLearner
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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 original paper: Collaborative Filtering for Implicit…
Fred Pym
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Normalizing product quantities to use them as implicit ratings in Product Recommendation

I am running a product recommendation using ALS method on retail transaction data. A simple question struck my mind on the using the methodology in case of implicit ratings. In my case I am using the quantities of different products bought by…
Aditi
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How does increasing the rank of latent factor model affect the bias-variance trade off in a recommender system?

I know overfitting means low bias and high variance while under-fitting means high bias and low variance. I want to understand how does increasing the rank of latent factor model affect the bias-variance trade off in a recommender system?
user2900761
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Most efficient way to set up a questionnaire to get to know a user's taste

I have a solid user-item matrix, with which I have build a collaborative filtering recommender system. I also have for each item a number of high quality features. If a new user comes to the website (online store), I quickly want to find out his…
Kasper
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Interpreting results of lightFM (factorization machines for collaborative filtering)

I built a recommendation model on a user-item transactional dataset where each transaction is represented by 1. model = LightFM(learning_rate=0.05, loss='warp') Here are the results Train precision at k=3: 0.115301 Test precision at k=3: …
Manas
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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 illustrated but I cannot reproduce the results. For item…
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