We’re rewarding the question askers & reputations are being recalculated! Read more.

Questions tagged [recommender-system]

Everything related to recommender systems

Filter by
Sorted by
Tagged with
0
votes
0answers
14 views

How to encode an array of categories to feed into sklearn

I'm working on a recommendation problem, broadly following the Youtube paper on theirs. Their surrogate problem is to recommend the next video a user will watch. One feature they include in their ...
0
votes
1answer
11 views

Value error in an embedding layer

I am new to deep learning and I am trying to build a book recommender system using embedding layers. I use one layer for the book and one for the user. I am having trouble with fitting the model. ...
0
votes
1answer
9 views

exercise 9.3.2 from mmds book

I am reading this book http://infolab.stanford.edu/~ullman/mmds/ch9.pdf there is an exercise 9.3.2 a) it says Exercise 9.3.2 : In this exercise, we cluster items in the matrix of Fig. 9.8. Do the ...
1
vote
1answer
28 views

How to draw neural network diagrams with this particular style?

I would like to draw a neural network architecture with the follow style. Do you know which tool can be used to do this? The paper is Operation-aware Neural Networks for User Response Prediction.
0
votes
1answer
21 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 ...
0
votes
0answers
17 views

nDCG - choose relevance scores

I am evaluating a recommender system using nDCG. The recommender system predicts similar movies for a given movie. I want to evaluate predicted similarity rankings by comparing them to a ground truth ...
0
votes
1answer
42 views

Does sum of embeddings make sense?

Referring to the LightFM model from paper Metadata Embeddings for User and Item Cold-start Recommendations, the model tries to learn $d$-dimensional user and item feature embeddings $e_f^U$ and $e_f^...
1
vote
0answers
25 views

Understanding reduced dimension embedding from tabular data

Background I am working on building a collaborative filtering recommender system in Keras for a school project, following an approach from this article. The approach is to take tabular user, item ...
1
vote
2answers
68 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 ...
8
votes
3answers
6k views

Can I use cosine similarity as a distance metric in a KNN algorithm

Most discussions of KNN mention Euclidean,Manhattan and Hamming distances, but they dont mention cosine similarity metric. Is there a reason for this?
4
votes
1answer
45 views

TS-SS and Cosine similarity among text documents using TF-IDF in Python

A common way of calculating the cosine similarity between text based documents is to calculate tf-idf and then calculating the linear kernel of the tf-idf matrix. TF-IDF matrix is calculated using ...
2
votes
1answer
57 views

Similar students using Machine Learning

I have a student performance data, where I have marks of various subjects for the students and I want to find similar students with good marks in a particular subject using machine learning. How do I ...
1
vote
1answer
46 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 ...
2
votes
1answer
220 views

Use of negative correlation coefficient in pearson correlation algorithm for recommender systems

I'm new in recommender systems and I try to find similar users of a base users for user-based collaborative filtering. When I calculated the similarity score now between two users (based on there ...
4
votes
2answers
156 views

Recommender system for next career step

I want to build a recommender system that suggests the next step in your career. About the dataset. About 50'000 Users with following informations: Skills (tags, string value) every job they did (...
0
votes
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 ...
2
votes
3answers
1k views

Can a recommendation system be built without any user ratings?

I was planning to make an artwork recommendation system as a project by using the WikiArt open source dataset available on kaggle, I'm still looking for datasets which might already have user ratings ...
0
votes
0answers
18 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 ...
2
votes
2answers
93 views

Matrix Factorisation Improvement

I am using SGD matrix factorisation (python) using the movielens dataset to make recommendations. I have a website which allows users to give feedback which is positive or negative to whether an item ...
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 ...
0
votes
1answer
65 views

Null predictions for ALS in Pyspark

I am trying to read from my dataset which has three coloumns. (User, Repository and Number of Stars) In[10] ...
0
votes
0answers
7 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: ...
1
vote
1answer
35 views

Creating a Feature to determine popularity

I am Building a Recommendation System in which i have Multiple Category , I want to Know how Popular is my Product in each Categories. For that I am considering Probabilty as one factor. For e.g I ...
6
votes
1answer
128 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
vote
0answers
18 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 ...
0
votes
2answers
125 views

Reduce data length to train effectively

I have customer buying data with each row specifying an item bought by customer. The problem is that even if at the same time customer buys five items then there are five different rows for it and as ...
0
votes
0answers
8 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 ...
2
votes
1answer
139 views

recommender systems : how to deal with items that change over time?

Let's say I am building a recommender system where items change through time. We suppose that each transaction is composed of : an item $i$ in list of items $(i_1, i_2, i_3, .., i_m)$. a user $u$ in ...
0
votes
1answer
32 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: ...
0
votes
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.
0
votes
0answers
22 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 ...
1
vote
1answer
381 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 ...
1
vote
1answer
47 views

How to recommend items after finding similar users in recommendation system

As the title explains my problem, I'm done with creating a recommendation system that can give me similar users for any given new user. The problem I face is, If I extract the list of products that ...
2
votes
1answer
53 views

Evaluating recommendations quality and accuracy

I'm developing a recommendation system, that should provide my clients what actions they should take in order to hit certain targets. The underlying mechanics of the process is physical - where both ...
2
votes
1answer
360 views

Neural network model for sparse multi-class classifier on Tensorflow

The problem I'm trying to solve is the following: the data is Movielens with N_users=6041 and N_movies=3953, ~1 million ratings. For each user, a vector of size N_movies is defined, and the values ...
1
vote
1answer
57 views

A weird result from a recommender system

Say there're the top 10 most popular items among 100 sales products and about 100k users regularly purchase items on daily basis. ...
0
votes
0answers
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-...
0
votes
3answers
35 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: ...
0
votes
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 ...
1
vote
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,...
3
votes
1answer
97 views

Neural Network - Sparsity of collaborative based filtering and modelling the prediction problem

I'm fairly new to machine learning and for that matter, neural networks, but for the past couple of days I decided to take a stab at a fairly classical and practical problem of neural networks/machine ...
0
votes
1answer
46 views

Learning similarity of representations

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 other recommender systems, which does ...
1
vote
1answer
246 views

What's the best classification model for this recommendation engine?

I'm not a data scientist but I'm trying to implement a recommendation engine on my company. My application runs on PHP but I'll use Python to process this data. My company is an online school, with ...
1
vote
1answer
42 views

Recommender system that connect users with each other , should I go for content based or collaborative filtering?

I am trying to build a system where user come on the platform and he chooses a topic(predefined few topics) and then we connect him with any random online user who chooses the same topic. Then they ...
5
votes
1answer
136 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: ...
0
votes
2answers
35 views

How do I correctly build model on given data to predict target parameter?

I have some dataset which contains different paramteres and data.head() looks like this Applied some preprocessing and performed Feature ranking - ...
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. ...
2
votes
1answer
133 views

Understanding Youtube recommender (candidate generation step)

I'm trying to understand https://storage.googleapis.com/pub-tools-public-publication-data/pdf/45530.pdf Their candidate generation step outputs topn items via softmax (with negative sampling) at ...
0
votes
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 ...
4
votes
1answer
341 views

How to deal with position bias in search?

In search, position of the search result affects the click-through rate a great deal. How do people usually deal with this ? In practice how to remove such bias to create unbiased training data for ...