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Questions tagged [recommender-system]

Everything related to recommender systems

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Recommender system based on user attributs [on hold]

I am working on a case study of a bank , I want to recommend product for other users based on users similarities This are the information i have: Age, Gender, City, Nationality, socio-professional ...
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How to build an AI powered search engine [on hold]

I am looking to develop a search engine drived by AI for an ecommerce web app, The aim of that work is to considere the user preferences when responding to the request of the user just like the ...
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Personalised search ranking for hotels

I've built hotel embeddings which gives very satisfactory results in returning similar hotels for each hotel. Now the problem I'm trying to solve is to rank the hotels in order of relevancy to the ...
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Is it a good idea to train Neural Network for classification on dataset where each document has a different class i.e. no class is repeated again?

My goal is to build a recommendation model for which I want to use Neural Network (LSTM). The user will give some input keywords and the model should return the suggestions (classes) based on ...
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How to validate recommender model in healthcare?

In order to validate a recommender model, a usual approach is create a hold-out set that will provide random suggestions (similar to an A/B testing setup). However, in healthcare applications, this ...
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How do I recommend items to out of training users based on its recent views?

I used Spark's ALS implementation of matrix factorization (Collaborative Filtering for Implicit Feedback) to train user and item embeddings. Since we have a lot of users in system, I had to sample ...
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Deep learning recommendation system

Please look at this problem: If I'm manufacturing lunches and wish to provide customer with a personalized lunch (not necessary an existing recipes). The customer will choose ingredients and will ...
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1answer
22 views

What is the range of values of the expected percentile ranking?

I'm currently reading Hu, Koren, Volinsky: Collaborative Filtering for Implicit Feedback Datasets One thing that confuses me is the "expected percentile ranking", an function the authors define to ...
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Understanding Youtube Recommender (item embeddings)

https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45530.pdf section 3.2 states that ...
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Understanding Youtube recommender (candidate generation step)

I'm trying to understand https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45530.pdf Their candidate generation step outputs topn items via softmax (with negative sampling) at ...
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86 views

Algorithm for SVD based recommendation engine

I am trying to build an SVD based recommendation engine for MovieLens database. After going through multiple online tutorials and resources I have understood how SVD works if a user-rating matrix is ...
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1answer
37 views

A weird result from a recommender system

Say there're the top 10 most popular items among 100 sales products and about 100k users regularly purchase items on daily basis. ...
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Mining Association rules from a data warehouse and a transactional database [closed]

I wonder if it is possible to perform market basket analysis to extract the association rules from a data warehouse and a transactional database in the same time to predict the future purchases of a ...
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How do I correctly build model on given data to predict target parameter?

I have some dataset which contains different paramteres and data.head() looks like this Applied some preprocessing and performed Feature ranking - ...
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2answers
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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 ...
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7 views

Null predictions for ALS in Pyspark

I am trying to read from my dataset which has three coloumns. (User, Repository and Number of Stars) In[10] ...
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23 views

Can transfer learning be applied to predict sales

Let matrix A be a user item matrix.Upon performing UV decomposition , I get a user factor matrix and factor entity matrix. The company I am interning at doesn't keep track of the user factor matrix.I ...
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Does cardinality of ratings column affect performance of matrix factorization based collaborative filtering?

I am using mllib's implicit preference based implementation of collaborative filtering for generating grocery product recommendations in e-commerce, based on this Netflix Prize winning algorithm. I ...
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23 views

How to training the recurrent recommender system with LSTM?

Recently, I read a paper about recurrent recommender system, I am very curious about how it training its network. Assume I have the Netflix dataset as ...
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0answers
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recommend new category paths based on factor item matrix and sales of the items

Matrix A be a user item matrix. Upon performing UV decomposition, I have just the V matrix. The matrix A differs every week and I get a new V matrix every week. The matrix U is not kept track of and ...
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27 views

Learning similarity of representations

I am interested in a framework for learning the similarity of different input representations based on some common context. I have looked into word2vec, SVD and other recommender systems, which does ...
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4answers
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How to match a user with another user based on their taste?

Information available Consider that there are N users on a platform. Every user adds items that they like on their profile. These items have static attributes that describe the product. ...
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1answer
28 views

How to match a user with other users with similar interests based on their attributes?

Information Available Consider, there are 'n' users and they have these attributes and values ...
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16 views

Does recommender system depend so much on the number of possible choices of the user?

Most recommender system have the motivation of building it because there are so many choices for the user. But I am thinking that even if there are few choices, the recommender system can make an ...
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Finding unobserved ratings using matrix factorization

Suppose that the utility matrix contains ratings of user on movies. How does matrix factorization find ratings for unobserved (user, movie) pair. Even though the purpose of the algorithm is to find ...
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Neighbourhood based approach on implicit feedback datasets

Is it possible to use a neighbourhood-based approach on implicit feedback dataset? I have a dataset with information about how many times a certain item was viewed by a particular user. I know that MF ...
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Including user-item pairs without interactions in implicit feedback dataset for recommender system

I have a dataset which contains information about how many times a particular user viewed certain item. So, I don't have rows for all combinations (where the value will be zero ofc because the user ...
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1answer
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How to learn irrelevant words in an information retrieval system?

Right now my recommender system for information retrieval uses word embedding stogether with Tfidfs weights like written here: http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-...
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Is it possible to rank feature importance after training a recommender system?

I need to train a recommender system on some movie recommendation data. The thing is, I wanted to use a random forest for the model, since I know you can print a feature hierarchy, after training. I'...
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2answers
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Classification model for recommender system?

I have some data for various customers choosing one of 'n' products or no product. I have some useful features for each customer. I can build a multi-class classification problem out of this data and ...
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Item item similarity from both user data and content

I'm creating an item-item similarity matrix by combining implicit user data and static content data of the items. How can I determine the weights for these two data and the weights for different ...
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17 views

Does sum of embeddings make sense?

Referring to the LightFM model from paper Metadata Embeddings for User and Item Cold-start Recommendations, the model tries to learn $d$-dimensional user and item feature embeddings $e_f^U$ and $e_f^...
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1answer
32 views

Similar students using Machine Learning

I have a student performance data, where I have marks of various subjects for the students and I want to find similar students with good marks in a particular subject using machine learning. How do I ...
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What methods exist for recommendation based on implicit information?

Assume we have a dataset of which products a user is using on a monthly basis. Let's further assume that the number of users is $n$ and the number of products is $p$ and that we are in the $p\ll n$ ...
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27 views

Job Recommendation System

I am building a Job Recommendation System where I have Student Data for different subjects in Machine Learning(Data Viz, Python, Statistics, etc) and their skills from the resume. Need to Recommend ...
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1answer
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How to select random data for two different recommender systems?

The business problem: We have two different vendors that offer personalized recommender engines and want to do A/B testing with them. The recommendation will give the user a personalized offer via a ...
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40 views

Matrix Factorization for Recommender Systems

Referring to the paper Matrix Factorization Techniques for Recommender Systems, Loss function for Matrix Factorization using bias terms is given as: $$ \min_{p, q, b}\sum_{(u,i)\in\kappa}(r_{ui} - \mu ...
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Regularization term in Matrix Factorization

I'm trying to build a naive recommender system using latent factor model for MovieLens dataset. From the observed set of ratings I'm trying to build a model which will decompose the sparse matrix to N ...
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1answer
24 views

Is this matrix correctly built?

I was reading an article called "Ensemble learning in recommender systems: combining multiple user interactions for ranking personalization" where they explain a method they use called "BPR ...
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1answer
59 views

Create recommendation system to recommend products to a customer on any e-commerce website

The recommendations should be based on the products consumer has searched on other sites like Google. This basically means, that recommendations have to be made to the user based on his/her search ...
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1answer
31 views

Creating a Feature to determine popularity

I am Building a Recommendation System in which i have Multiple Category , I want to Know how Popular is my Product in each Categories. For that I am considering Probabilty as one factor. For e.g I ...
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2answers
335 views

Can a recommendation system be built without any user ratings?

I was planning to make an artwork recommendation system as a project by using the WikiArt open source dataset available on kaggle, I'm still looking for datasets which might already have user ratings ...
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49 views

How do I perform Leave One Out Cross Validation For Top n Recommendation Sytems?

I am new in making recommendation systems . I am using the surpriselib library to evaluate my recommendations. All the Accuracy Metrics are well supported in this library. But I also want to compute ...
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1answer
56 views

recommender systems : how to deal with items that change over time?

Let's say I am building a recommender system where items change through time. We suppose that each transaction is composed of : an item $i$ in list of items $(i_1, i_2, i_3, .., i_m)$. a user $u$ in ...
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187 views

cosine similarity between items (purchase data) and normalisation

I'm using IndexedRowMatrix which represents the products's user purchase behaviours and in order to build product recommendations, I use cosine similarity to calculate similarities between products. ...
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How do I store/model data needed for my recommendation module?

I'm reading data from a store's product catalog, a 100mb xml file which contains product-wise attributes like prices, categories, etc. Given a product_id, my job ...
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1answer
130 views

Does a matrix factorization recommendation engine use user/item related features?

All the tutorials I can find about matrix factorization recommendation systems start with importing users, items, and user-item-ratings, but then only use the rating matrix to train the recommender (...
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1answer
40 views

Evaluating recommendations quality and accuracy

I'm developing a recommendation system, that should provide my clients what actions they should take in order to hit certain targets. The underlying mechanics of the process is physical - where both ...
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1answer
93 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 (...