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Questions tagged [recommender-system]

Everything related to recommender systems

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2
votes
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 ...
11
votes
4answers
13k views

Can I use cosine similarity as a distance metric in a KNN algorithm

Most discussions of KNN mention Euclidean,Manhattan and Hamming distances, but they dont mention cosine similarity metric. Is there a reason for this?
4
votes
2answers
98 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 ...
0
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1answer
32 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 ...
1
vote
1answer
30 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 ...
1
vote
1answer
31 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 ...
1
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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
votes
1answer
67 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
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2answers
57 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 ...
4
votes
1answer
51 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]], ...
1
vote
1answer
38 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 ...
0
votes
1answer
105 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 ...
2
votes
0answers
97 views

Multidimensional collaborative filtering model

I have a dataset that is approximately structured in the following way: 500 users, 500 products, 100 countries, 2 seasons, 300,000 ratings. Meaning that I have 300,000 rows containing unique ...
3
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2answers
2k views

Train-Test split for a recommender system

In all implementations of recommender systems I've seen so far, the train-test split is performed in this manner: ...
2
votes
1answer
258 views

How do I predict a set of frequently bought items?

I have a dataset of retail transactions wherein different users buy certain items together. For example, a user A buys a toothpaste, a toothbrush and a floss at the same time, and a user B buys a ...
1
vote
1answer
34 views

Recommendation system with active learning

I have data where companies ask users to score a bunch of questions but some items may be randomly chosen while others are personalized. Users score higher in personalized questions on average. I have ...
0
votes
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
vote
1answer
27 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 ...
0
votes
1answer
31 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 ...
4
votes
1answer
41 views

User-to-item and item-to-user recommendations

I'm currently creating a recommender system and there are different types of the systems. Does anyone know something about the user-to-item and ...
3
votes
1answer
68 views

Recommender Model for Human Action in Income Protection

Problem Domain I'm working on a project that involves building a model to provide recommendations on the next best step for Human supervisors to take on income protection claims. Income protection is ...
2
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2answers
484 views

Offline evaluation of recommender systems

Let's say I want to compare whether one recommender system (A) is better than the other (B). One approach is to let people rate recommendations returned by both systems. However, there situations ...
4
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1answer
93 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 ...
0
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0answers
26 views

Learning to Rank vs Reinforcement Learning in Information Retrieval - which one is preferable and why?

I am trying to create an information retrieval system which can benefit from user feedback (either implicit, through e.g., click-through data) or explicit (e.g., binary feedback on irrelevant ...
0
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0answers
20 views

product recommendation of a single product based on customer similarity?

I have been wondering how you can build a model to recommend only one single product to a bunch of customers. So basically the question that I would like to answer with this model is to have a ranking ...
3
votes
4answers
4k 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 ...
1
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0answers
15 views

Item-to-Item similarity: is weighted sum the most popular approach?

In content-based filtering (CBF) recommenders, when there are is no user profile, similar items are recommended an item that a user is currently inspecting. For instance, if you are looking for a ...
0
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0answers
37 views

Human intuition behind SVD in case of recommendation system

I checked the SVD for recommendation engine thread but it does not answer my question. I struggled very hard to understand the SVD from a linear-algebra point of view. But in some cases I failed to ...
1
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0answers
20 views

Recommendation Systems User Profile Streaming Data on GCP

I have a recommendation system that recommends articles to different users. I am planning to provide the recommendations in an off-line fashion. Where I already have a table in BigQuery which has the ...
1
vote
1answer
233 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] ...
1
vote
1answer
48 views

How to include user features in a recommender system?

I'm novice in that matter but I was thinking about the formulation of a recommender system. Let's take the example of a movie recommendation system. We have a column dedicated to movies ID (or names), ...
0
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0answers
8 views

Get latest Item by Date for a Recommender System

I am building a Recommender System where I am giving the User 3 Recommendations depending upon for the Webpage he is on. Let's say My model gives me 3 Recommendations from 2020, 2019, 2015. I would ...
5
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1answer
485 views

How to use ndcg metric for binary relevance

I am working on a ranking problem to predict the right single document based on the user query and use the NDCG metric to measure the model. Given the details : Queries ( Q ), Result Document ( D ),...
0
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0answers
19 views

Classify Spanish Text into different Categories

I want to recommend articles to users depending upon what type of article is user reading, Music, Movies, Politics, etc. I have 3 features: Page Title, Labels, article content. I am using an API (...
0
votes
1answer
23 views

How to filter Items in Recommender Systems?

I have a Recommender System which recommends Articles based on Similarity from 3 Features, "Page-Title, Article Content, Tags". But some of the Articles are NSFW(Related to Adult Topics). I ...
0
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0answers
22 views

What are the Objectives for Recommendation Systems and what Key Results should a Recommendation System focus on?

I know that a Recommendation system helps in the engagement of the users and helps users find more relevant content but I am in search of more complex objectives and key results with regards to ...
0
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0answers
9 views

Link Recommendation System

I am trying to build a Recommendation system on my website for recommending similar articles to the user. Eg: Lets say a user is reading an article about sports on a news website. The next article ...
10
votes
1answer
9k views

How to split train/test in recommender systems

I am working with the MovieLens10M dataset, predicting user ratings. If I want to fairly evaluate my algorithm, how should I split my training v. test data? By default, I believe the data is split ...
2
votes
1answer
4k views

memory error in matrix cosine_similarity

I have (20905040, 7) of a dataset to recommend 10 different product to the user it could be larger than that but anyway I got memory error when processing the ...
4
votes
3answers
103 views

Make use of relationships on recommendation systems

I have a data set of user rating for movie as user_name, product_name, user_rating and I am using this data to recommend new movie to user (collaborative ...
15
votes
2answers
28k views

Item based and user based recommendation difference in Mahout

I would like to know how exactly mahout user based and item based recommendation differ from each other. It defines that User-based: Recommend items by finding similar users. This is often harder to ...
32
votes
4answers
15k views

Meaning of latent features?

I am learning about matrix factorization for recommender systems and I am seeing the term latent features occurring too frequently but I am unable to understand ...
6
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1answer
478 views

Understanding Youtube recommender (candidate generation step)

I'm trying to understand Deep Neural Networks for YouTube Recommendations. Their candidate generation step outputs top N items via softmax (with negative sampling) at training time . via ...
1
vote
0answers
28 views

Building a content-based recommendation system using products' metadata as features?

I am currently working on an apparel recommendation system, where I have tabulated data containing a list of products with their respective metadata (brand, category, color etc.) I have an additional ...
0
votes
0answers
19 views

Can previous successful actions be used as input to policy model in contextual bandits?

In a recommender application, I apply contextual bandits using logged propensity scores similar to this. The model is retrained daily. The application recommends images on an e-commerce website. Each ...
0
votes
0answers
7 views

Can I use LSI (Latent Semantic indexing) to get similar docs for several documents at the same time?

I'm working on a Recommander system in which I'm using LSI to get similarities between videos. I wonder if I can provide to LSI matrix more than one document and get similar docs for all those. In the ...
0
votes
1answer
25 views

How to calculate similarity between 2 users based on the images they share?

Say there are 2 users, A and B, and they each shared 10 images (in some social media site), which I have collected in 2 folders separately. I want to calculate the similarity between the 2 users based ...
1
vote
0answers
139 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 ...
0
votes
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 ...
0
votes
0answers
218 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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