Questions tagged [recommender-system]
For questions related to recommender systems in the context of machine learning and data mining.
27
questions
2
votes
1answer
51 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?
0
votes
0answers
7 views
How to train-test split and cross-validate in surprise? [migrated]
I wrote the following code below which works:
...
0
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0answers
11 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 ...
0
votes
0answers
9 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
vote
1answer
27 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
22 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.
1
vote
0answers
33 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 ...
1
vote
2answers
40 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
13 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
29 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.
...
1
vote
1answer
18 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 ...
1
vote
0answers
7 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
67 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 ...
2
votes
1answer
54 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 ...
1
vote
0answers
9 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
9 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
184 views
How do I plot a matrix of ratings?
I have a .csv file called ratings.csv with the following structure:
...
1
vote
1answer
110 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 ...
1
vote
0answers
14 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
93 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
165 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
57 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
80 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 ...
1
vote
0answers
51 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
votes
1answer
38 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, ...
-2
votes
1answer
45 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
217 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 ...
