Questions tagged [recommender-system]
For questions related to recommender systems in the context of machine learning and data mining.
30
questions
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0answers
36 views
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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0answers
14 views
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 ...
3
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1answer
56 views
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:
...
1
vote
1answer
45 views
What is meant by the rank of the scoring function here?
I've been reading this paper on Knowledge Graph Reasoning for Explainable Recommendation lately, and I don't understand a particular section:
Specifically, the scoring function $f((r,e)|u)$ maps any ...
2
votes
1answer
64 views
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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20 views
When has RL helped in building Recommender Systems?
I was wondering if it'd be possible to list some or all the instances wherein Reinforcement Learning has been used to build Recommender Systems - here's one paper I've already come across, and I found ...
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0answers
55 views
Why is diversity of reasoning paths important in recommender systems using knowledge graphs?
This is a continuation of the discussion that originates on this StackExchange post, about recommender systems using knowledge graphs(KGs). For those who might not prefer reading the original post, I ...
1
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1answer
53 views
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 ...
1
vote
1answer
36 views
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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0answers
51 views
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 ...
2
votes
2answers
53 views
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 ...
2
votes
0answers
19 views
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 ...
1
vote
1answer
32 views
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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1answer
23 views
Code freezes and never returns when linear_kernel (sklearn.metrics.pairwise) is used on 20M Movielens dataset
I'm fairly new to ML/AI, i'm trying learn the content based recommendation - here is my source code - https://github.com/jaganlal/content-based-recommender
I'm using MovieLens 20M dataset - tags.csv ...
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0answers
10 views
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
2
votes
0answers
74 views
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 ...
3
votes
2answers
80 views
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 ...
2
votes
0answers
12 views
Mobile App Recommendation: How to get the rate of a specific user submit for a specific application
I have a mobile app recommendation project, so I need data set which has user-app matrix-rate. Actually, I want to know what rate does a specific user submit for a specific application. in other words,...
1
vote
0answers
11 views
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 ...
1
vote
2answers
191 views
How do I plot a matrix of ratings?
I have a .csv file called ratings.csv with the following structure:
...
1
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1answer
120 views
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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0answers
16 views
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_{...
5
votes
1answer
111 views
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 ...
2
votes
1answer
274 views
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-...
1
vote
1answer
59 views
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 ...
2
votes
1answer
89 views
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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0answers
58 views
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 ...
0
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1answer
40 views
Recommendation system based on content type
I am new to this field and would like to know that for what kind of data types other than images, recommendation system can be created using machine learning. Suppose for contents like audio or video, ...
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votes
1answer
50 views
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 ...
5
votes
1answer
225 views
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 ...
