Questions tagged [recommender-system]
For questions related to recommender systems in the context of machine learning and data mining.
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Is item-based collaborative filtering the same thing as content-based filtering?
According to this Google dev page
content-based filtering
Uses similarity between items to recommend items similar to what the
user likes.
collaborative filtering
Uses similarities between queries ...
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How to update item and user factors ALS in Group Specific Recommendation?
I have also asked this question on our Data Science site.
I was going through this Group Specific Recommendation System paper. I want to implement this from scratch. I see that they have used ...
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How are session-parallel mini-batches used for training RNNs for session-based recommender tasks?
I am reading this paper on session-based recommenders with RNNs: https://arxiv.org/abs/1511.06939. During the training phase, the authors apply what they call "session-parallel mini-batches,"...
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How to build/train a recommendation system model with no user related data
I have a set of items with their attributes. I want to use this data to train a an open-source model that uses users-items interactions as positive preferences data.
Would it be a good idea to ...
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Nearest-Neighbour recommendation distance ranking where some recommendations occur more than once
A nearest neighbour recommendation engine may, given a query vector identify the ranked top-K nearest vectors within a dataset using a distance measure such as cosine similarity. In my problem domain, ...
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How do we give recommendations when users create/post content (like in YouTube)?
I've explored tools like amazon personalize, etc. for generating recommendations. It seems like amazon personalize is appropriate when all the content is with the company/a single entity. For example, ...
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A recommender system based on millions of fields including text and number
I want to train a model based on millions of fields, including text and number, that are stored in a SQL database and recommend a perfect match based on some inputs. Now, which algorithm is the best ...
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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 ...
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Hyperparameters for Reproducing the Results of IRGAN on MovieLens 1M
I am trying to reproduce results reported for IRGAN (information retrieval GAN) on the MovieLens 1M dataset. The results I want to reproduce and their sources are listed in the table below.
Model
...
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Recent methods for Decision Support System (DSS)
In Decision Support System (DSS), we rank items based on predetermined weighted criteria. For example, we want to rank prospective programmers based on their working experience, required salary, set ...
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what is the correct approach for KNN in item based recommendation system?
if I make an application for movies and each user in the system can rate the movies. And I want to make a recommendation system to recommend movies to active user based on his rating for other movies. ...
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How can I build a recommendation system that takes into account some constraints or the context?
I am building a recommendation system that recommends relevant articles to the user. I am doing this using simple similarity-based techniques (with the Jaccard similarity) using as features the page ...
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Human intuition behind SVD in case of recommendation system
This 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 connect the dots. So, I started to see all the ...
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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:
...
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What is meant by the rank of the scoring function here?
I've been reading the paper Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (by Yikun Xian et al.) lately, and I don't understand a particular section:
Specifically, the ...
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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?
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Which reward function works for recommendation systems using knowledge graphs?
I've been reading this paper on recommendation systems using reinforcement learning (RL) and knowledge graphs (KGs).
To give some background, the graph has several (finitely many) entities, of which ...
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What are multi-hop relational paths?
What are multi-hop relational paths in the context of knowledge graphs (KGs)?
I tried looking it up online, but didn't find a simple explanation.
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Why can't I Hyper tune my KNNBasic Algorithm?
I've been trying to hyper tuning my KNNBasic algorithm by the help of grid search for recommendation system for movie review data. The problem is that both of my KNNBasicTuned and KNNBasicUntuned ...
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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 ...
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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 ...
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How to get top 5 movies recommendations from Auto-Encoder
I have trained a model using Auto-encoder on movielens dataset. Below is how i trained the model.
...
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Anyone familiar with Bilateral Recommendation System? And suggest any related papers?
I'm working on Bilateral Recommendation System. But not able to find much related papers. Could anyone suggest any papers relative?
Thanks
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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 ...
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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 ...
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Reducing the Number of Training Samples for collaborative filtering in 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 ...
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How do I plot a matrix of ratings?
I have a .csv file called ratings.csv with the following structure:
...
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184
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Which model is better given their training and validation errors?
Below you have the plots of the training and validation errors for two different models. Both plots show the RMSE values for the validation dataset versus the number of training epochs. It is observed ...
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26
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Estimating Baselines using ALS
I am trying to figure out how ALS works when minimizing the following formula:
$\\ \\$
$\text{min}_{\lbrace b_u,b_i \rbrace} \sum_{(u,i)\in \mathcal{K}} (r_{ui} - \bar{r} - b_u - b_i )^2 + \lambda_{...
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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 ...
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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-...
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Why is KNNBasic better than KNNWithMeans with the default parameters, but KNNWithMeans performs better with folds?
I'm learning a bit about the use of the Surprise library and I have a set of data with users and ratings. I'm training a network with this library, using KNNBasic and KNNWithMeans, this last algorithm ...
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63
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Learning similarities between customers and offers representation
I am interested in a framework for learning the similarity of different input representations based on some common context. I have looked into word2vec, SVD and siamese networks, all of which are ...
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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 ...
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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 ...
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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 ...
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Can recommendation systems be created for other data other than images?
Can recommendation systems be created (using machine learning) for other data other than images?
For audio or video content, is it necessary to use a dataset of actual audio and video files, ...
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Over-exposure of certain items in content based recommendation engine
I'm working on a content based recommendation engine for ebooks. I create document vectors with 300 features for every ebook using a word2vec model trained on google news and determine recommendations ...
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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 ...
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