Questions tagged [recommender-system]
Everything related to recommender systems
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Matching items in a recommender system
I would like to ask for a proposal for a machine learning model that would be suitable for the following problem:
I have a training set where each element of type A corresponds to a certain number of ...
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How to add query filter to the Nearest Neighbors algorithm?
I have Nearest Neighbors model, built with sklearn sklearn.neighbors.NearestNeighbors, which I use to make content based recommendations.
Sometimes I need to ...
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In-batch Random Negative Sampling
I'm trying to train a recommender model using In-batch Random Negative Sampling as described in the following paper: https://arxiv.org/pdf/2102.06156.pdf. I'm having a bit of difficulty wrapping my ...
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Rules/Guidelines for Custom Weightage and Hyper-parameter tuning
I have a movie and user-ratings dataset. After implementing the content-based filtering technique, I figured, I can improvise the results even further by assigning weightage to the parameters based on ...
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Clustering methods for text and image features
I want to build a recommender system with unlabeled data and used TF-IDF to extract text features from a given short description and VGG-16 to extract image features. I am looking for a way to combine ...
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Learning to Rank Suitability
Say there is an intermediary company that offers different kinds of loans from a number of banks. My job is to determine the order/ranking in which the bids should be displayed to the customer.
For ...
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Why is a model server needed to generate feature IDs in Bytedance's Monolith system?
I was reading Bytedance's paper on real-time recommendation systems (https://arxiv.org/pdf/2209.07663.pdf) and I was confused by the figure on page 2.
In the Online Training Stage, why do we need the ...
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27
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How to build a Resume-to-Job Description matcher based on a parsed JSON Resume dataset?
For my capstone project/internship I'm working on an "HR assistant" tool designed to help match, score and rank resumes given a job description and/or requirements. I have inquired about ...
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Which is the loss function used for validating a CF Recommender System?
I am developing (from scratch) a memory-based CF Recommender System based on movielens dataset.
My CF RS uses a URM (User Rating Matrix) where r_ij contains the rating the user i gave to movie j (or ...
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Product/Consumer tag weights from previous sales
Problem statement
I have a series of products that have been assigned tags, resulting in a vector of ones and zeros for each product (1 = product has this tag, 0 = product does not have this tag).
I ...
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Obtaining labels to compute recall@ for recommended systems
Let's say we have a recommended system with 2 steps: candidate selection and ranking. For candidate selection we want to have high recall, where we can define recall as number of items recommended ...
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recommender engine with ALS and expired products
I built a recommender engine with ALS (using implicit) to recommend web articles to a given user. The articles expire after a while (a couple of weeks) and I'm using several months worth of data to ...
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Resources for Promotion/Demotion Strategies for ML Item Recommendation Systems?
We are looking to design a system where specific items or categories of items can be boosted/promoted up or relegated/demoted down the recommendation order.
What are the common strategies or standards ...
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Difference between recommender system and appetency score
I'm wondering about the difference between the recommendation system and the appetency score. I already know that the appetency score is a binary classification problem for one product where we try to ...
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Using RecSim trained agent in production
I am new to using Reinforcement Learning in Recommender Systems.
Can someone please give me pointers on how to use an agent trained using Google's Recsim in production?
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Meaning of unit weight for negative impressions in 'Deep Neural Networks for YouTube Recommendations'
I'm having some trouble understanding this section of the paper:
Deep Neural Networks for YouTube Recommendations
4.2 Modeling Expected Watch Time
...
The model is trained with logistic regression ...
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Applying feedback in content based recommendation
I have a content based recommender system, which finds similar items given a list of past liked items using cosine similarity.
What would be best way to implement feedback or personalization in the ...
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Cluster products that are frequently bought together
I have a dataset of articles metadata for each article, so something like this:
product_id
color
type
1234
red
t-shirt
and another containing the transactions of customers, which looks like this:
...
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Any ideas on deciding a hotel competitive group?
Suppose for each hotel, I need to find a group of hotels which are competitive to this hotel. Competitive hotels mean they probably share the same group of customers, and the customers will make a ...
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IllegalArgumentException at org.apache.xbean.asm5.ClassReader.<init>(Unknown Source) when training an ALS implementation of spark in scala
I was following this tutorial trying to write a collaborative recommender system using the alternating least squares algorithm in spark. I am using the movie lens dataset which can be found here.
My ...
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Evaluate a Recommender System based on the data between two months
currently my company's planning to use a new Recommender tool/library for a book website, and now we want to compare the result between these two tools (both of the tool use Universal Recommender ...
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How to add significance weighting in user based collaborative filtering
I have been learning about recommender systems these past days. More specifically about the collaborative filtering. While exploring I found that it can be useful to use "significance weighting&...
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Customizing Collaborative Filtering for Product Affinity
I'm trying to build a recommendation system and I am trying to use Collaborative Filtering (please let me know if other models fit better for my use case).
My Data:
My data is for an e-commerce site ...
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Why is accuracy not a useful measure for information retrieval problems?
I have been studying about information retrieval and recommender systems. While reading about it I found that accuracy not a useful measure in information retrieval.
I understand that, accuracy might ...
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In what cases does the addition of a nonlinearity decrease neural network performance?
I have a simple model, which learns well. It is a two tower recommender where we maximise dot product between positive pairs. The current structure is just an embedding layer followed by a dense layer ...
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Ensemble model for a recommender system
Suppose I have three models - A, B, and C - all of which are good candidates for a Recommender system. I want to build an ensemble, combining the three models, but I'm not sure how to proceed.
Suppose ...
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Should I train the model on the whole dataset in recommender systems?
After reading some tutorials and articles about recommender systems, I can't really figure out whether I should split the dataset into train/test sets or use the whole dataset to allow the model to ...
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Recommendation System | Collaborative Filtering | User-User Filtering
Apologies in advance if this question is broad or basic for data science community.
Given:
Dataset containing thousands of lines with Apache HTTP Server log file produced in Common Log Format (CLF) ...
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Minimum number of items required to build a good hybrid recommender system?
I am trying to build a hybrid recommender system using lightFM that only recommends one of $3$ items. In my case, they are marketing campaigns that a company would like to recommend for users at a ...
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information retrieval vs recommendation system
Apologizes in advance, if this question is so basic,
Problem:
I have read this paper and noticed that Information Retrieval can be identified as a field of study whereas Recommender Systems are a ...
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Weighting features in Jaccard Distance (1-hot-encoding)
I one-hot-encoded features and want to calculate similarity with the Jaccard index. But I am 100% sure that features have different importance for my clustering (i.e. some features are more important ...
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What is n_factors in surprise SVD
The documentation of Surprise library is not that great. Can someone please help with details of n_factors in SVD method of Surprise. It simply says:
n_factors – The number of factors. Default is 100....
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Nearest Neighbor Recommendation System w/ categorical variables
I would like to build a recommendation system:
no ratings are available at the time of recommendation, therefore only a purely context-based recommendation system is needed
as input features answers ...
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Personalized Recommendations In Content Based Recommendation System
I'm trying to create a content based recommender system. The system accuracy is quite enough when finding similar items but it's not as good as when recommending items to a specific user.
I use ...
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What is degenerate feedback loop and how to detect and prevent it?
The degenerate feedback loop often makes machine learning models fail, then how to detect and prevent it?
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How to build a personalized recommendation system using real life scenario data?
I am trying to build a recommendation system to recommend items to users. This is the kind of real-life example I am trying to implement. But anywhere I searched about making a recommendation system ...
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How to recommend movies for users which are not in trainset with Surprise SVD Matrix Factorisation algo?
I'm trying to implement a Surprise SVD movie recommender system and finally deploy it on a website, where the user would rate, say 10 movies and get back the top 10 recommendations. Since I already ...
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Adjusted Cosine Similarity With Zero Vectors
I create a recommendation engine which finds item similarities according to user ratings. I'm trying to use adjusted cosine similarity to find similarities. I follow these steps.
Find mean rating of ...
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We pose recommendation as extreme multiclass classification problem, what is a class here? is it video category? or the video itself?
In the Youtube video recommendation paper, the author talks about candidate generation is a multi class classification problem, I am trying to understand what the classes here, a video category or the ...
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How do I know if user or product attributes are useful in a hybrid recommender?
I'm building a hybrid recommender for recommending products to users for a retail business. The model includes transactional data, as well as user and product features. How do I know if a user/item ...
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Do recommender systems predict only from test sets?
I have read about recommender systems. Something I can't understand is if they predict from the test dataset or if they can predict the training dataset too. I thought they fit the model on the ...
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Recommend low cost channel based on customers demographics
I am new to data science.
I am trying to implement recommendation system which only uses customer demographics data and activities like gender, age, customer transaction frequency in last 1,3 and 6 ...
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How to proceed when training data change frequently (in production)?
I'm working with a Recommendation System that would take as parameters a bunch of "tweets" a user see during his navegation on a mobile app. Every tweet has a property, like a category (...
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Recommender test set of 1%
I've built a lightfm recommender and want to evaluate test set performance. However, my dataset is quite large, so it takes prohibitively long to calculate precision at k for my test set. I was ...
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Is it ok to weight a transaction matrix twice?
Say I have a matrix with 1 row per customer, 1 column per product. A 0 if a given customer has not bought a given product, and a 1 if they have.
I want my recommender to prefer newer products, so I ...
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What goes into a test set for a recommender in LightFM?
I am building a recommender using LightFM. My data is implicit, with a 1 if the customer bought a product and a 0 if not.
I have split my data into test set and training set, where test set is all my ...
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Ensuring recommender doesn't just pick top items
When building a hybrid recommender for products, how do you ensure that the model doesn't just recommender the most popular products all the time? Would throwing out X% (where X is high) of ...
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Categorical features in Factorization Machines
How should categorical features be encoded to substitute values for x_i and x_j when modeling Factorization Machines? The large number of categorical variables makes one-hot encoding impractical.
...
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Compute similarity with given weights for each different feature
I would like to find similar products based on the features.
I have:
3 categorical features (X1,X2,X3)
1 numerical (continuous) feature (X4)
1 date feature (X5)
Therefore, I want to give a pre-...
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Supervised recommender system design feedback
I am facing a challenge that I am not quite sure how to solve and would like to hear feedback.
Basically, I have to implement a recomendation system for certain courses to be recommended to users of ...
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