Questions tagged [recommender-system]
Everything related to recommender systems
383
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5 views
Methods to generalise NCF recommender systems to unseen users, same set of items?
I'm new to recommendation models, and am starting to build a recommender system on the MovieLens dataset using NCF-style model.
As I'm building it I'm wondering if, once trained, I can apply it to my ...
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0answers
18 views
How to get KNN linearly hybridised by two similarities?
I'm writing a KNN (collaborative filtering) hybrid similarity recommender and I need some advice. It is based on this paper.
I've currently got 2 datasets. The first one is ...
0
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1answer
19 views
Cold start recommender system with features
I have to develop a recommender system where most of the users only buy 1 item, so I have a cold-start problem. For this reason, I'm discarding matrix factorization techniques and content-based ...
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17 views
How metric learning works for content based item retrieval
I was doing some computer vision experiments and recently I have started learning about metric learning and the image retrieval problem. I was experimenting with the inshop image retrieval dataset to ...
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8 views
Model performance in different snapshots varying
I am trying to solve this problem. A medical representative needs to visit some doctors' clinics and for that a model will generate probability scores for visiting a clinic. I ma using a tree based ...
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1answer
24 views
Calculate implicit rating from streaming behaviour for Recommendation Engine
I have a dataset containing some user streams data for particular videos like below:
...
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1answer
36 views
Two-tower net does not learn when made deep
I have been trying to train a relatively simple two-tower net for recommendation. I am using PyTorch and the implementation is the following - basically embeddings layers for users and items, optional ...
1
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1answer
22 views
Changing behaviour of an ML model
I am trying to create a ranking system for recommending books to an user. Let's suppose we have some subjects of books like 'A', 'B', 'C', 'D' and from the past behaviour, it is observed that the user ...
1
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1answer
21 views
Integer encoding and weighing when one feature consists of more names [closed]
Hello I am trying to make a content based movie recommendation system and one feature is genre of the movie. I will give an integer number to each genre randomly. However, some movies are of more than ...
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1answer
45 views
precision@k and recall@k
Normally, I am familiar with precision and recall evaluation metrics but as you know recall@k and precision@k are different things and used in ranking evaluations especially recommendation systems.
I ...
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21 views
What is the right approach to bucket users for algorithms with different coverage for A/B testing
I've couple of recommendation algorithms that I want to A/B test. Algorithm A has 90% user coverage and algorithm B has 95% user coverage. That means if the algorithms are asked to provide ...
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0answers
24 views
LightFM implementation for scala/java on spark
I am looking for hybrid recommendation libraries such as lightfm that I can use on Spark (with Scala).
Any alternative?
Or best would be for me to build a hybrid recommendation system on spark's mllib ...
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0answers
25 views
Cross validation for Collaborative filter-based recommendation systems
I am an absolute beginner and am trying to implement collaborative filter for furniture ecommerce (think wayfair). I need some guidance about cross-validation strategy.
Situation:
I am working on a ...
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0answers
30 views
How does an autoencoder 'fill in the blanks' in the context of a recommender system?
My understanding is that an autoencoder takes an input, produces a lower dimensional representation of the input, which should explain the original features in the dataset, and then reconstructs the ...
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20 views
how to calculate the cosine similarity between two files?
I am using spark and scala to implement an issue. files contain phrases or sentences. I want to use domain based method to calculate the cosine similarity between tags.I convert two files into a ...
1
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1answer
34 views
Temporal train test split for recommender systems
When evaluating a collaborative filtering recommender system, it is practical to split the data temporally. However, by doing so, some users might be present in only either of the train or test set. ...
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0answers
46 views
Can we use embeddings or latent vectors for a recommender system?
I'm having a hard time understanding why people use any vector they find as a candidate for a recommender system.
In my mind, a recommender system requires a space where distance represents similarity....
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13 views
Neural Recommendation System - Explanation
Hello I am working on a recommendation problem in which I want to recommend the next best product to a customer. I am using a collaborative filtering approach but I would like to have as a result, the ...
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0answers
21 views
How to use sklearn's Matrix factorization to predict new users' scores
I'm trying to use sklearn.decomposition.NMF to a matrix R that contains data on how users rated items to predict user ratings ...
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34 views
Weighting of features in Recommender Systems
I'm new to Recommender Systems, and wanted to figure some things out in order to make the best possible Content Ranking System. I want to make a ranking of all the content (and content providers) ...
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14 views
What are the business metrics I should track to evaluate a recommender model deployed on an e-commerce website? [closed]
Can you suggest some google analytics metrics such as (click or impressions etc) to evaluate a recommender model deployed on an e-commerce website.
1
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1answer
28 views
How to find coherence between a large number of sentences
I have a list of sentences returned as a result of a document search algorithm. I want to determine if the results returned are semantically close/similar/coherent using some sort of metric. For a ...
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0answers
19 views
Can latent factor model work for new users?
I am studying latent factor model for recommendor system. It does matrix factorization(like SVD) on the user-item rating matrix. What I am not sure is, does a trained model work for a new user that is ...
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0answers
20 views
(Graph Convolutional Network (GCN) based recommender system maintenance issue [closed]
I have built an item-item recommender model using (Graph Convolutional Network (GCN) for an E-commerce website. Could you please help me with the maintenance of the model.
How often should I retrain ...
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0answers
13 views
estimate user's satisfaction of a video based on how much of it they watched - normalization
I am trying to estimate how much a user liked a video using how much of the video they watched.
Let's say, on the scale of 1 to 10, 1 means that the user didn't like it at all, and 10 means they ...
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0answers
6 views
How is SVD from scikit-surprise handles empty values
I am studying the surprise lib for recommender system. SVD from this lib doesn't require all value input in user-item matrix. But it is a must of the original SVD method. The official doc doesn't ...
2
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1answer
23 views
Reduce serving time complexity for real-time recommender systems
I am working on a real-time recommender system predicting a product to a user using deep learning techniques (like wide & deep learning, deep & cross-network etc). Product catalogue can be ...
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1answer
43 views
How to model a supervised recommender system with varying data
Suppose there are 2000 movies and a company wants to recommend some movies (for example, at most 5 movies) to each visitor. The objective is to learn how to predict which movie will be selected if a ...
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11 views
Top n products as a kind of recommendation system
I'm looking for a paper, book or something similar that describes only the top products as a kind of recommendation in a recommender system. The top products can be determined with a simple counter. ...
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0answers
14 views
Why I need to generate train instances and load negative samples?
If you look at this GitHub link ( here is the paper link for the implementation ) you can see that the get_train_instances method generates trainingns instances. In ...
2
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1answer
25 views
Recommender/Clustering data to support a hypothesis. Is this a valid use-case for unsupervised ML?
I have a dataset where some items have been labelled (categorized into 4 classes [A,B,C,D]). However, there is a vast majority of the dataset which has not been labelled. My hypothesis is that there ...
8
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3answers
461 views
Why is deep learning used in recommender systems?
I am currently reading a lot about recommender systems (RS) and came across that many RS are based on deep learning.
However, I never find a good scientific article why deep learning is used in RS and ...
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0answers
22 views
Scoring metric for recommendation system
I'm working on a project that involves building a news recommendation system. I've come as far as quantifying user interaction with different articles on the site into user's affinity towards atopic ...
0
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1answer
30 views
How to grade an interaction that a user had with a post with an AI based on big data?
Context
I'm creating a social network.
The thing is, I don't want to order posts by likes, or something like that, I'm using an AI (lightfm in ...
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1answer
29 views
Finding and ranking best semantic matches between two sets of phrases
I'm looking for a proper definition for what sort of problem this is, so I can further research it on my own - though I will, for sure, appreciate any specific advice on what are industry standard ...
4
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2answers
94 views
What is the best model for a recommendation system using implicit ratings?
I have a similariy matrix that looks like this:
I have a bunch of user vectors with 1s and 0s, with a 1 indicating that someone has clicked on an email (as part of a campaign) and zero to indicate ...
1
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1answer
25 views
Evaluation of recommendation systems
I have developed a content-based recommendation system and it is working fine. The input is a set of documents={d1,d2,d3,...,dn} and the output will be Top N similar documents for a given document ...
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0answers
10 views
Predict the target audience for a new brand using data from other brands and customers buying behavior
Assume a company has a large database about wine, including brand, the taste of the wine, year, place of production, etc, and data of customers' purchase behavior. Now if there is a new brand coming ...
0
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1answer
56 views
How can collaborative filtering be extended to include more features?
Looking at the following:
https://realpython.com/build-recommendation-engine-collaborative-filtering/#using-python-to-build-recommenders
I can see that userID, itemID, rating are the standard features ...
1
vote
1answer
37 views
Recommender System for mostly unique user and items
I am trying to develop a recommender system for a job matching problem. My data consists of past matched candidate profiles and job profiles as well as if there was a success such that both, candidate ...
1
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2answers
50 views
Evaluate document similarity / content-based recommender system
I'm planning on building a basic content-based recommender system with word2vec and cosine similarity.
The data consists of 300k documents in varying length.
How do I evaluate my model if I have no ...
0
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1answer
69 views
LightFm - replicate precision@k score with predict vs. predict_rank method
LightFm has two methods to predict: predict() and predict_rank(). The evaluation function ...
4
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1answer
47 views
Why softmax in YouTube’s DNN recommender
I am confused about the softmax layer of YouTube’s DNN candidate generation. A user may interact with many videos. Softmax is assuming classes are exclusive. For example, logits = [[4.0, 4.0, 1.0]], ...
0
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1answer
47 views
How to estimate missing values when calculating NDCG
I would like to compare recommendations methods using NDCG metric on MovieLens dataset.
In ranking problem, the goal is to rank items based on their relevance for user. Ranking models can be learned ...
2
votes
2answers
118 views
Which metrics for evaluating a recommender system with implicit data?
I am currently in the process of creating a recommender system. This recommender system works with a neural network and then searches for the closest neighbors and thus gives recommendations for a ...
0
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0answers
11 views
Adding additional information in content-based recommendations
I have a book dataset where 100 users have rated the books as like/dislike. Each observation with features
Table1 : ['user_id','book_name', 'book_genre','author','date_published','like/dislike']
These ...
1
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1answer
128 views
Proper evaluation method for recommendation system with implicit feedback?
I am trying to implement a recommendation system for a live-streaming website. Here "users" are simply the website users and "items" are streamers that they should watch. I ...
0
votes
1answer
23 views
finding similarity of a new datapoint
I have built a recommendation engine using cosine similarity. When I want to find all the records similar to a given record that is already present in the dataset it works. Consider a case, a user ...
1
vote
1answer
76 views
Click Through Rate calculation (CTR) calculation problem
So I'm doing a use case for a company interview and one of the questions is to calculate the CTR for a sorting algorithm.
My question would be: Should I remove the operations where there were no ...
0
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1answer
30 views
How to represent genre or artist name in a neural network
I am writing a music recommendation system using machine learning. I'm attempting to make sense of ensemble networks to allow the system to learn from both the content-based features, as well as the ...
