Questions tagged [recommender-system]
Everything related to recommender systems
288
questions
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7 views
For the following data set, what is the best approach to build a recommendation system?
Data set
P.S I wish to build a recommendation system that will recommend items based on the criteria preferences of users.
How do i approach this?
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1answer
19 views
On the offline evalution of recommender system
There are mainly three ways to evaluate a recommender system: offline, online and user study.
For most academic papers, offline evaluation is used to show the improvements:
They split the offline ...
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0answers
9 views
play session from ratings dataset in Movie20 M
I need to extract listening sessions from the ratings dataset which has the columns
cols = [userId movieId rating timestamp]
timestamp is just a number for eg 1112486027 listening sessions are ...
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0answers
14 views
Recommender system heavily biased towards popular items
I am training a pure collaborative filtering recommender system on MovieLens 1M using Tensorflow-Ranking. I use embeddings to represent users and items and feed concatenated embeddings through two ...
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0answers
6 views
How to validate collaborative filtering recommender system in r?
I have a project which I have to make a recommender system of BX Books dataset. I use cosine similarity as my algorithm. I came up with this script of R:
...
6
votes
1answer
116 views
Understanding the softmax output in Youtube's recommender
This question has been asked before, but never (that I can see) satisfactorily answered.
I'm reading Youtube's paper on their recommender system. The system has two elements, the first of which is a ...
1
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0answers
17 views
Transfer Learning and Recommender Systems
I have a task in which I am pretending to have an "unobserved" system, let's call it the target system, that I am using an LSTM from a similar system that has observations to perform the regression. I ...
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0answers
7 views
Testing Setup in Temporal Tensor Factorization for Recommendation
I am trying to wrap my head around Temporal Tensor Factorization for Collaborative Filtering, as described in the paper: Temporal Collaborative Filtering with Bayesian Probabilistic Tensor
...
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1answer
30 views
Understanding the “Wide” part of Google's wide and deep
Google's wide and deep recommender model sounds really cool, but I'm struggling to believe I'm grasping the wide section right so wanted to check my understanding.
Their paper says the following:
...
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0answers
13 views
Can we treat sentiment score of the review text as rating of the product?
I have a review text of the different products, And I need the rating of the product. So can we use sentiment score as the rating of the product.
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21 views
Recommendation system without user data
I have a recommendation project to build without user data.
Suppose I am building a initial recommendation system with the initial dataset with me only consisting of
...
0
votes
1answer
29 views
When to stop showing content on recommendation engines?
Let's take an example. I log into my Netflix account and see that it's suggesting the show Friends to me. But I have no interest in watching Friends. So I ignore it. The next time I login, it suggests ...
1
vote
2answers
63 views
Recommendation system depend on user rating and favorite list
In my project I have a database of Japanese Sake(rice wine).Each Sake has following attributes which has direct link to the taste of the Sake:
classification (enumeration 1-5 integer)
alcohol ...
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11 views
SVD++ in matrix form
I have been recently reading the following presentation and papers preceding it:
http://staff.ustc.edu.cn/~ynyang/group-meeting/2014/matrix-factorization/Matrix-factorization-recommendation-system-...
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0answers
29 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,...
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0answers
12 views
In learning latent factors in matrix factorization, are weights learned based on similarity between e.g. users?
I implemented a recommendation system using user-user interaction data, learning missing ratings through alternating least squares and matrix factorization, which as I understand it, adjusts and ...
4
votes
1answer
91 views
How is the cross-product transformation defined for binary features?
I am reading the paper on Wide & Deep learning and for the wide component, it states that one of the most important transformations is the cross-product transformation. This is defined as follows:
...
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0answers
14 views
supervised similarity scoring system - recommender system
I have a dataset of movie collections with 10-15 features describing each movie. I also have a dataset of user ratings of the similarity between some random pairs of movies.
Using both of these data ...
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0answers
16 views
What is the difference between row-wise and column-wise Z-score normalization?
I have a data set, each row represents a movie name, each column is a feature (such as genres), I want to perform cosine similarity to find out the similarity between each movie, before that I need to ...
3
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0answers
38 views
What is the “matrix trick” in recommendation systems?
I just found slides from Matt Gormley (CMU) about recommendation systems. Under the heading "Unconstrained Matrix Factorization" he mentions:
Optimization problem
SGD
SGD with Regularization
...
1
vote
1answer
276 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
...
0
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3answers
33 views
If I have to recommend 10 movies to the users
Let's say I have some information about a user and movie data similar to the following:
...
2
votes
1answer
38 views
Item-based recommender using K-NN
I'm trying to build an item-based recommender using k-nn. I have a list of items, all of which have some properties (features) in common.
...
1
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0answers
21 views
How to perform Learning to Rank for a small dataset
I am very interested in applying Learning to rank to my problem doamin. When I read through the literature of Learning to rank I ...
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0answers
5 views
How to validate model which outputs movies that are similar with no training data
At a high level, my code takes a wide variety of movie related features and computes a large cosine similarity matrix and assesses which movies are most similar.
I don't have any validation data so ...
0
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1answer
103 views
Predict Customer Next Purchase with Sequence
Suppose I buy products: [1,2,3,4]
Another customer X bought: [2,3]
Most probably customer X next purchase will be: 4
Sequence is very important in my problem
I tried association analysis using R, ...
1
vote
0answers
16 views
Reducing the Number of Training Samples for collaborative filtering 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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0answers
42 views
what will be the best algorithm for find similar users based on user features , betwenn knn, euclidian or jaccard?
I have user data table containing user data like university, gender, city, id etc. I also have a matrix data-set like this .
[
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1answer
22 views
learning a distance metric from the output of a knn output
Suppose that someone has trained a nearest-neighbor algorithm based on some unknown metric. I have a large dataset of $N$ observations, and $P$ features. For each observation I am given $K$ indices ...
0
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1answer
57 views
Student-Teacher maching problem
The problem I want to solve is as follows:
I have data about how many teaching hours different students spend with their teacher in order to pass their exams. The teaching varies from 25-55 hours. In ...
1
vote
0answers
29 views
What is the current state of the art solution for Movielens 100k / 20M?
I found Basic recommendation system for Movilens dataset using Keras which has a solution which works ok (MAE 0.84).
What is the current state of the art for this dataset?
2
votes
1answer
38 views
How does recommendation by matrix factorization deal with new movies / users for which there are ratings?
Assume you have the ratings of $n$ users for $m$ movies in a matrix $R \in \mathbb{R}^{n \times m}$. You compute a representation
$$R = U \times \Sigma \times V$$
by initializing $u_i, v_j \forall i ...
0
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0answers
7 views
Recommender with global explicit feedback
I've to build a recommendation system with a dataset where the feedback is given for the whole set of items instead to the specific items. We can see that feedback as a numerical indicator of the ...
0
votes
1answer
12 views
Document matching with more priority to certain features than others
I am working on recommendation systems wherein I need to match the similarity of 2 users. Now, I know that I can use Tfidf vectorizer to calculate the the cosine similarity score between them. But, ...
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0answers
18 views
Handling collection of featurevectors for classification
I have a data set where devices are represented by a collection of variables. These variables consist of several properties like a name, datatype, driver, limit values, etc. (mixed data; quantitative ...
4
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6answers
169 views
Distance between users
I want to compute the "distance" between users in order to return the top n similar users, for any given user.
For each user a have a bunch of features.
This is close to a recommendation system, ...
0
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0answers
8 views
Building a dynamic recommender system that changes based on how users rate
I am trying to build a recommender system for coding interview questions.
I thought of doing a collaborative-filtering, but it is hard to get data on how other users rated the questions.
I currently ...
1
vote
1answer
30 views
Recommender systems and Machine Learning
According to Mitchell:
“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T , as measured by P , ...
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0answers
22 views
Building a personalized recommender system for coding interview questions?
I am trying to build a recommender system for coding interview questions.
Let's say I have data for interview questions and the features are
...
0
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0answers
11 views
Any library recommendations for a Python recommender framework with multi-class input, incl. array input?
(crosspost from https://softwarerecs.stackexchange.com/questions/62491/python-recommender-framework-with-array-input, this meta post seems to justify doing this. I really really need answers to this, ...
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0answers
21 views
How can you recommend songs based on a user's past listening history by genre (content filtering)?
I'm interested in getting a user's past listening history from Spotify (API call to recently played) and being able to suggest songs from the Charts (another API call for current chat listings) that a ...
0
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0answers
38 views
Building recommendation engine from transactions data
I am trying to build a recommendation engine for an e-commerce company and I have the following input files :
1) past user transactions + in-app events
2) a new list of campaigns I should recommend ...
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0answers
7 views
How to reduce complexity of inference stage in recommender systems?
Given a large set of customers and a large set of items, how to make predictions given a model like this one:
https://arxiv.org/pdf/1606.07792.pdf
As stated in the article:
"Since there are over a ...
1
vote
1answer
67 views
Use machine learning to predict next schedule meeting for sales officers
I have a project with data of sales field officers who visit their customers and enter the progress details. Visit can be an order or any kind of customer interaction.
Let's say one sales guy has ...
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0answers
11 views
We know the subspace generated from the data instances, but we cannot constitute the origin space
I was wondering, what if we know the subspace generated F from the data instances, but we cannot constitute the origin space E that can be in higher dimension, and can easily lead us to the true join ...
2
votes
1answer
36 views
Calculate a ranking function from classification features
I am using 3 features (x1, x2, x3) for binary classification. All my feature values are in 0 to 1 range (unit range).
I obtained how important each feature was in ...
1
vote
1answer
45 views
How to use testing data set to measure recommender system algorithm
I am new to recommender systems and am trying to build one using item-to-time CF. Currently, I am trying to evaluate/measure results using MAE. I have one step which is unclear (after I managed to ...
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0answers
15 views
What is the accepted level of persona coverage for Recommender Systems?
Almost all of e-commerce companies use recommender systems which involve a set of personas. (explained in this post).
"Your personas will never cover 100% of your users"
In practice, what is the ...
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0answers
60 views
scala spark FP-Growth no results displayed
I have implemented the FP-growth algorithm and it works fine with this sample data:
r z h k p
z y x w v u t s
s x o n r
x z y m t s q e
z
x z y r q t p
when I ...
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0answers
35 views
I got a overall mean average precision score of 0 for a recommendation engine
I just wanted to know if receiving an overall MAP score of 0 in a recommendation engine was possible, or a sign that my calculation or my logic for the engine was wrong.
