Artificial Intelligence Stack Exchange
Artificial Intelligence Stack Exchange

Questions tagged [recommender-system]

For questions related to recommender systems in the context of machine learning and data mining.

See e.g. https://en.wikipedia.org/wiki/Recommender_system.

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How do recommendation systems work?

How do recommendation systems (e.g. on Youtube) work? Apparently, every user gets different recommendations depending on his location, his past liked videos, etc. So it would seem like a training model is applied to every single user, but that can't…
Guest2000
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Cold start collaborative filtering with NLP

I’m looking to match two pieces of text - e.g. IMDb movie descriptions and each person’s description of the type of movies they like. I have an existing set of ~5000 matches between the two. I particularly want to overcome the cold-start problem:…
Derek Hans
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What is the best way to find the similarities between two text documents?

I would like to develop a platform in which people will write text and upload images. I am going to use Google API to classify the text and extract from the image all kinds of metadata. In the end, I am going to have a lot of text which describes…
Nehemia Litterat
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What is the most appropriate ML algorithm for creating recommendations

I am trying to find the best algorithm to create a list of recommendations for a user based on the interests of all other users. Say I have a list of of samples: $samples = [ ['hot dog', 'big mac', 'whopper'], ['hot dog', 'big mac'], …
zen
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Which machine learning algorithms can be used to build a recommendation system?

I am working on building a recommendation engine. I need to build a model that recommends similar items. Currently, I am using the Nearest Neighbor algorithm present in sklearn.neighbors package. I am working in finance domain, similarity can based…
Harshith
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What are some limitations of using Collaborative Deep learning for Recommender systems?

Recently I worked on a paper by Hao Wang, Collaborative Deep learning for Recommender Systems; which uses a two way tightly coupled method, Collaborative filtering for Item correlation and Stacked Denoising Autoencoders for the Optimization of the…
m2rik
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How to design a recommendation system for shift swapping?

I need to design an algorithm such that it handles the request for shift swapping. The algorithm will recommend a list of people who are more likely to swap that shift with the person by analyzing previous data. Can anyone list the techniques that…
Paras
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Nearest neighbour search in high dimension retrieves certain points too often

We represent some catalogue items (documents, music tracks, videos, whatever) as vectors embedded in R^d and use them to retrieve nearest neighbours to users query. The typical scenario is that users can input any query and the search results are…
Peter Franek
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How matrix factorization helps with recommendations when it converges to the initial user-items matrix?

We can say that matrix factorization of a matrix $R$, in general, is finding two matrices $P$ and $Q$ such that $R \approx P.Q^{T}$ with some constraints on $P$ and $Q$. Looking at some matrix factorization algorithms on the internet like…
KindNewbie
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Why can't pure KG embedding methods discover multi-hop relations paths?

According to Reinforcement Knowledge Graph Reasoning for Explainable Recommendation pure KG embedding methods lack the ability to discover multi-hop relational paths. Why is it so?
stoic-santiago
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Using AI to enhance customer service

I'm trying to find out how AI can help with efficient customer service, in fact call routing to the right agent. My usecase is given context of a query from a customer and agents' expertise, how can we do the matching? Generally, how is this problem…
Tina J
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How to model personalized threshold problem with machine learning

Assume that I have a candidate selection system to generate product/user pairs for recommendation. Currently, in order to hold a quality bar for the recommended product, we trained a model to optimize for the click of the link, denoting as…
Jason
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When is content-based more appropriate than collaborative filtering?

I know the difference between content-based and collaborative filtering approach in recommender systems. I also know some of the articles said collaborative filtering have some advantages than content-based, some of them also suggest to use both…
malioboro
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How do stacked denoising autoencoders work

I've been studying a recommender system which uses a collaborative deep learning approach and Bayesian learning. It has the following NN representation : I need to know the working of stacked denoising autoencoders. Here is the link to the…
m2rik
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what is tfrs.metrics.FactorizedTopK in tensorflow recommenders

from the official documentation link In our training data we have positive (user, movie) pairs. To figure out how good our model is, we need to compare the affinity score that the model calculates for this pair to the scores of all the other…
Bharathwajan
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