Questions tagged [recommender-system]

Everything related to recommender systems

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0answers
140 views

Data augmentation for recommendation systems

I have a user-item matrix that I use to train a denoising autoencoder to predict the top-k items to recommend to the different users. The idea is to corrupt the matrix by erasing a percentage ...
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0answers
15 views

Machine Learning Recommender high data intensity

We are building a recommender engine to be integrated in an app that, each time an API is called, will pull thousands of records from an Azure SQL database and create recommendations. Currently with ...
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0answers
224 views

User-Item based Recommendation system with data containing binary data

I have a data set which contains about 400,000 unique items present on a platform. The users on this platform can like and save this in their own list. Now, I have about 4000 users with their like ...
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0answers
66 views

Predicting Item Ratings with the Log Likelihood Ratio

I'm trying to infer prediction ratings from an item-item similarity matrix where the similarity score is calculated via the log-likelihood ratio (LLR). I'm using this code snippet to calculate the LLR ...
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0answers
19 views

How to use the position factor in known data as a feature in recommendation surfacing?

The problem is recommending stories on a website, just below each story based on how similar the stories are and some historic data based on what recommended stories were clicked or not clicked. So ...
3
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1answer
222 views

How to build recommendation model based on resume and job description?

How to build a model which will result in better recommendation of resumes based on the job description given? I am familiar with bow or tfidf (n-grams) approach and then take a cosine similarity but ...
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1answer
2k views

How to calculate NDCG in recommendation system

This is a question about NDCG, which is a recommendation evaluation metric. The following are being used as evaluation indicators for recommendations. $$DCG = r_1 + \sum\limits_{i=2}^{N}\frac{r_i}{...
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0answers
38 views

How to train a recommender system to improve the customer class?

Considering the definition of a recommending system A recommender system, is a subclass of information filtering system that seeks to predict the "rating" or "preference" a user ...
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3answers
582 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 ...
3
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2answers
248 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 ...
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1answer
119 views

BPR TripletLoss Recommender System

I am trying to modify the code of this repo to build a recommender system based on BPR triplet loss. In particular I modified the TripletLoss layer class like this ...
1
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1answer
50 views

Learning Resources for Recommendation system

Beginner here: Could you please suggest some of the learning resources (books/youtube/articles) for beginners who want to build a recommendation system for their organization. Have no clue about it ...
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1answer
1k views
0
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1answer
292 views

Building a content-based music recommendation system

I am trying to build a recommendation system in Python that recommends songs based on a playlist. What I have is two datasets: 1. One dataset consists of 350 songs from my playlist and 13 acoustic ...
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3answers
9k 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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0answers
413 views

System Requirement to train BERT model

How much Hardware is required to train it well?(My current PC specs: 8GB RAM, i5 2 core Processor, Standard GPU (No work going on GPU)) I have a dataset of approx 1lakh records.Is it is necessary to ...
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0answers
595 views

How to create a model and make predictions with LightFM?

I've been researching on how to develop a hybrid recommender system for a simple book dataset, the main goal is to use both explicit data (purchases) and latent factors (features) to make the ...
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0answers
37 views

Ranking graph's nodes by score propagation

Problem I have the following directed tripartite graph $G(E\cup V\cup P, A)$, where there is a many-to-one symmetric relationship between the subsets V and E - $e\in E,v\in V,[e, v]\in A \iff [v, e]\...
1
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1answer
905 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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0answers
24 views

How to make a popularity-based recommender system having data on posts and number of likes? Please review a code

I'm writing a popularity-based recommendation system, where I have data on posts and likes the posts have. I need to recommend posts to a user based on their popularity (obtained likes). Packages and ...
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0answers
14 views

Factorization Machines with some some pairwaise effects and some linear effects

I have some covariates $x_1$, $x_2$, $x_3$, .., $x_{10}$. I want a linear term for all these covariates and a pairwise effect for $x_1$ and $x_2$, and I do not want any other pairwise effect. Is there ...
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0answers
22 views

Upsell project based on sales records

in my company we are working on a upset project in which we are trying to solve the following problem: What we propose to our customer that he/she may be interested in based on the fact that he/she ...
1
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0answers
57 views

How effective would this pseudo-LDA2Vec implementation be?

For my site I'm working on a chat recommender that would recommend chats to users. Each chat has a title and description and my corpus is composed of many of these title and description documents. I ...
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0answers
43 views

Reducing the Number of Training Samples for collaborative filtering recommender systems

I have the following problem: I am doing some research on the accuracy of recommender algorithms that are mostly used nowadays. So, one way to measure their performance is by checking how well they ...
1
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0answers
78 views

Combine features in Machine Learning KNN

I'm trying to build a simple book recommendation system, where I don't have any kind of ratings (no comments, no likes, no 1-5 stars, ...). The information I can use is the following: Book metadata ...
3
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2answers
469 views

Building a tag-based recommendation engine given a set of user tags?

Basically, the idea is to have users following tags on the site, so each users has a set of tags they are following. And then there is a document collection where each document in the collection has a ...
5
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2answers
876 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 ...
1
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1answer
442 views

Advantages of Binary Rating System for Collaborative Filtering Recommender Systems

I notice that Netflix, which I think used to use a five-star scale for rating content and give predicted ratings for unrated content on the same scale, now just has basic like/dislike buttons. Music ...
0
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1answer
63 views

Student-Teacher matching problem

The problem I want to solve is as follows: I have data about how many teaching hours different students spend with their teacher in order to pass their exams. The teaching varies from 25-55 hours. In ...
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0answers
55 views

Building a recommender system [closed]

I want to build a recommender system for shops, where I recommend items. I've learned about these systems like with content-based, collaborative filtering and so on. But now I want to make one on a ...
1
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1answer
71 views

In recommender systems, how to avoid recommending a product that the user has just bought?

Suppose I'm running an online store that sells many products, but from only a couple of categories, say: $A$, $B$, or $C$. Let's say a user has bought a product in the A category, and there's no ...
1
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1answer
73 views

NCF Recommender- The target encoded within the model input, why doesn't it overfit easily?

In the recommender system NCF, the input is a batch of user-item interactions (one-hot encoded) and the output is a 0-1 score of whether the item has been bought or not: This seems to indicate that ...
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0answers
56 views

Deal with huge amount of data

I'm writing to get advices about my project. I want to make recommander system for shop with some products. In fact i want to recommand to shop A to take item X because shop B sell this item and ...
2
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2answers
153 views

How to calculate precision at K and NDCG for ranking algorithms

I am ranking a filtered item list as per user's metadata and historical behaviour. Now how to calculate metrices like precision at K? One approach could be - Divide historical data in training and ...
1
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2answers
52 views

How to encode an array of categories to feed into sklearn

I'm working on a recommendation problem, broadly following the Youtube paper on theirs. Their surrogate problem is to recommend the next video a user will watch. One feature they include in their ...
2
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1answer
27 views

Recommender system that matches similar customers with similar highly rated products?

I have a dataset of 1,000 customers that bought 20 distinct phones and rated them 1-5. I have several demographic attributes for these customers (gender, age). My website offers 100 distinct devices, ...
2
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0answers
26 views

Operations on Recommendation Embeddings

I've trained a recommendation system to recommend steam games based on game tags. An example output is shown below, where GAME is the game recommended based on the <...
4
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1answer
2k views

How to choose negative examples for recommendation system?

I am building a search recommendation system for e-commerce which generates most relevant results given an input query. I have framed it as a classification problem (learning to rank) and using ...
2
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2answers
91 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 ...
0
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1answer
46 views

CV(Curriculum vitae) Recommendation System guidance

I am building a recommender system which matches people's CV with a vacancy. So far, I used TF-IDF & Cosine Similarity to get a matching score between a vacancy and a candidate's CV. I want to ...
2
votes
1answer
358 views

Calculating Rank Ordering Error Metric for implicit recommendation

I'm reading Collaborative Filtering for Implicit Feedback Datasets. On page 6 they detail their evaluation strategy, which they define as mean Expected Percentile Ranking with the following formula: $...
5
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1answer
85 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 ...
1
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1answer
52 views

Using Amazon Personalize to build a Recommendation System

I would like to build a recommendation system based only in the items metadata. I have an input vector with some desirable topics that the user want to read about, for example: (self-help, yoga, ...
2
votes
1answer
368 views

Vectorizing equation in MATLAB

I am working on collaborative filtering using matrix factorization in MATLAB. I am using Gradient Descent for parameter learning. The cost function to optimize is : $ J = {\left\| I \odot (R - U V') \...
1
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2answers
59 views

Reverse engineering a distance metric from the output of a k-NN

Suppose that someone has trained a nearest-neighbor algorithm based on some unknown metric. I have a large dataset of $N$ observations and $P$ features. For each observation, I am given $K$ indices ...
6
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1answer
521 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 ...
0
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1answer
70 views

Value error in an embedding layer

I am new to deep learning and I am trying to build a book recommender system using embedding layers. I use one layer for the book and one for the user. I am having trouble with fitting the model. ...
1
vote
1answer
2k views

How to draw neural network diagrams with this particular style?

I would like to draw a neural network architecture with the follow style. Do you know which tool can be used to do this? The paper is Operation-aware Neural Networks for User Response Prediction.
1
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0answers
45 views

Understanding reduced dimension embedding from tabular data

Background I am working on building a collaborative filtering recommender system in Keras for a school project, following an approach from this article. The approach is to take tabular user, item and ...
1
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2answers
102 views

Recommendation system depend on user rating and favorite list

In my project I have a database of Japanese Sake(rice wine).Each Sake has following attributes which has direct link to the taste of the Sake: classification (enumeration 1-5 integer) alcohol ...

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