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Questions tagged [recommender-system]

Everything related to recommender systems

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11 views

We know the subspace generated from the data instances, but we cannot constitute the origin space

I was wondering, what if we know the subspace generated F from the data instances, but we cannot constitute the origin space E that can be in higher dimension, and can easily lead us to the true join ...
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1answer
38 views

Calculate a ranking function from classification features

I am using 3 features (x1, x2, x3) for binary classification. All my feature values are in 0 to 1 range (unit range). I obtained how important each feature was in ...
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1answer
46 views

How to use testing data set to measure recommender system algorithm

I am new to recommender systems and am trying to build one using item-to-time CF. Currently, I am trying to evaluate/measure results using MAE. I have one step which is unclear (after I managed to ...
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15 views

What is the accepted level of persona coverage for Recommender Systems?

Almost all of e-commerce companies use recommender systems which involve a set of personas. (explained in this post). "Your personas will never cover 100% of your users" In practice, what is the ...
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71 views

scala spark FP-Growth no results displayed

I have implemented the FP-growth algorithm and it works fine with this sample data: r z h k p z y x w v u t s s x o n r x z y m t s q e z x z y r q t p when I ...
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39 views

I got a overall mean average precision score of 0 for a recommendation engine

I just wanted to know if receiving an overall MAP score of 0 in a recommendation engine was possible, or a sign that my calculation or my logic for the engine was wrong.
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257 views

Cosine similarity with arrays contaning NaN

I am trying to calculate a cosine similarity using Python in order to find similar users basing on ratings they have given to movies. As it can be expected there are a lot of NaN values. I am using ...
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1answer
26 views

How many time a recommender system can recommand the same item to an user?

I'm working on an hybrid music recommender system project, my goal is to create recommendation playlists in accordance with users tastes. I already implemented the first part which use a collaborative ...
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18 views

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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42 views

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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1answer
27 views

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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19 views

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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1answer
51 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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1answer
133 views

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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1answer
57 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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34 views

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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2answers
35 views

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
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 ...
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1answer
65 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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0answers
33 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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7 views

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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79 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
21 views

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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1answer
46 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
66 views

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
38 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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0answers
20 views

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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20 views

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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0answers
24 views

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 ...
2
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1answer
79 views

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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17 views

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
62 views

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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2answers
3k views

Mean Average Precision python code

How do you compute MAP in python for evaluating recommender system effectiveness? Is there any library in sklearn or code in python for it? I would like to compute the effectiveness of my Recommender ...
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18 views

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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1answer
44 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
57 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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10 views

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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0answers
51 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 ...
2
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1answer
54 views

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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50 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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106 views

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
28 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 ...
3
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1answer
248 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
35 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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3answers
1k 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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0answers
148 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 ...
2
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1answer
139 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 ...
2
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1answer
372 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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14 views

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 ...