Questions tagged [recommender-system]
A recommendation engine tries to predict how much a user will enjoy certain goods (movies, books, songs, etc) and makes recommendations. They are often used by online vendors to suggest new purchases.
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15 views
State of the art on the recommendation system [on hold]
I want to create an ambitious recommendation system. My objective is to approach the state of the art in this field.
I have identified 2 interesting approaches:
The customer data based approach (...
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15 views
Recommender system with extra variables
I would like to to create a recommendation engine that makes use of a utility matrix (user-item interactions) as well as supplementary features (user features, item features and time-based features). ...
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17 views
Is it useful to add a proportion hyperparameter in the concatenation layer?
I'm reading a paper on deep learning-based recommender systems: Neural Collaborative Filtering. There are two sub-networks, GMF and MLP, which are fused into a unified model, by a concatenation layer. ...
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15 views
Matrix formation and calculation for Collaborative filtering in Neural Network
Intution
I am trying to implement Collaborative Filtering (User Based and Item Based) in Python Keras with neural networks.
For user based CF with neural network, my input is rating matrix with ...
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16 views
Building a job recommendation system [closed]
I am building a job recommendation system where in i have the required skills for each role along with the value of the skill relevant for the role. Example of the dataset is as shown in the image. ...
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1answer
67 views
Implicit Feedback Factorization Machines : Format of Input and Recommendations
I am looking at methods for making product recommendations to customers based on attributes of the customer (e.g. demographics) and past product interactions (e.g. did or did not buy). There are ~250 ...
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14 views
Algorithmic recommendations for adaptive content suggestion
I am interested in learning the ins and outs of adaptive content suggestion (similar to what facebook, google ads, youtube, netflix, linkedin and similar services typically do). I am new to the topic ...
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10 views
Is there weightage given to number of items in Association rules (market basket analysis)?
I have a data frame
...
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16 views
Clustering/Similarity between drivers
I have a dataset that contains initial and ending points of car trips:
...
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18 views
How SVD factorisation -based recomendation algos deal with new user interaction
Classic SVD and SVD++ alogritms generate predictions based on a current known ratings only for known users and known items. But I need to make prediction for some new user on the old items. In the ...
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11 views
Assigning weights to the features used in content based recommendation
I am trying to make a recommendation engine for book business which has following features associated with the books:
Book Region
Book Market Segment
Publish Date
Book Genre
Book Type
and so on
...
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21 views
Factorized matrix for recommendations, what then?
I have a dataset that looks like this:
Image taken from this blog
Let's assume that I have applied Matrix factorization and have learned the zero values for the items missing for every user.
I now ...
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103 views
Does anyone know the rank of the Netflix Prize dataset?
I'm looking into the Netflix Prize at the moment. We model the dataset as an $n \times m$ matrix, where $n$ is the number of users and $m$ is the number of movies. Does anyone know the rank of the ...
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45 views
Learning similarity of representations
I am interested in a framework for mapping together input representations based on some common context. I have looked into word2vec, which does more or less what I want, but I want to know if anyone ...
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41 views
Data normalization for recommender system
Does anyone know whether it's a good idea to standardize your data by replacing it with the percentile which it occupies in a distribution? Instead of substracting the mean and dividing by standard ...
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1answer
43 views
Offline evaluation of Counter factual data for Recommendation
I am building new model and facing at offline evaluation tasks.
My goal is to predict higher CTR(=click/impression) advertisement, and improve sales.(sales would improve if user watch more ...
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22 views
How to evaluate the goodness of a KNN-based recommender system?
I'm building a content-based recommender system and I'm using KNN. That means, for each test instance I'm using KNN to find the most similar objects, and then come up with a recommendation. However I'...
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37 views
Is there a well established algorithm to match two documents on a semantic level?
I have a set of documents from a wide variety of topics and I would like to retrieve the ones that are more similar to a new document provided. A search based on common words is not good enough, so ...
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34 views
Classification model for recommender system?
I have some data for various customers choosing one of 'n' products or no product. I have some useful features for each customer. I can build a multi-class classification problem out of this data and ...
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19 views
Build customer ratings from subscription datetime data for a recommendation system
I want to build a recommendation system with only some customer's subscription and unsubscription date. I have a database that looks like:
...
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28 views
How to build a recommendation engine using a regression algorithm?
I'm working on a recommendation problem in which I want to recommend the best setting/configuration for a machine, i.e. what wheel material results in longer run life for a machine (machine can be ...
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7 views
Predict / recommend products based user historic purchases
I am making a trend based product recommendation based on order data of users.
I have users which has a trend of buying item A everyday, item B every third day, item c after 10 days and so on. I ...
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24 views
Recommender using classification
Hi I'm tasked with building a recommender for predicting item categories to users.
I have data about which items the user has viewed and bought respectively.
I'm interested in turning this into a ...
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1answer
63 views
what is latent feature in collaborative filtering
What are latent features in collaborative filtering algorithms? I've been reading about it but don't really understand it. Are latent features the learned matrices from matrix factorization; the ...
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23 views
How to tune an exponential smothing function pon implicit feedback collaborative filtering recommenders
I am developing a recommender based on implicit feedback. The feedback is mainly the time someone spends on a product in a day. Then I transform this feedback to a rating matrix in order to implement ...
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1answer
48 views
How can I build a recommender system that accepts data of varying granularity?
I'm tasked with building a recommender system that can make recommendations based on input data of varying levels of granularity. To explain what I mean, let's use a running example of a Movie Title ...
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16 views
Pairwise recommender system
How can I model the following situation? I am looking for high level recommendations.
if say a man with certain attributes (income, age, etc ...) rates a car with a given attributes (color, ...
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9 views
Learn mapping function item to item
I'm trying to resolve a problem in which a set of items have to be mapped to another set of items. The mapping has to be one to one and the items have attributes that describe them (e.g. name, ...
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49 views
How to normalize data while training implicit data by Alternating Least Squares
I have an order table stored user buying history.
I want to use Alternating Least Squares to do collaborative filtering,
but there is no score, which make me have to use product buying count as score....
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39 views
Personalize Recommendations for small dataset
I'm working on a recommender system for a set of niche products. These are products that don't have a large number of customers. Does anyone have any tips on algorithms or approaches that work well ...
2
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1answer
77 views
Amount and sparsity of data for recommender systems
I'm starting to work in a project that will have a recommender system as one of its components. I'm trying to figure out if I have the right type of data for the recommender.
The data contains ...
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1answer
113 views
Why the maximum sparsity is 99.5% in collaborative filtering? [duplicate]
From https://jessesw.com/Rec-System/ says
98.3% of the interaction matrix is sparse. For collaborative filtering to work, the maximum sparsity you could get away with would probably be about 99.5% ...
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1answer
48 views
How do recommender systems incorporate user characteristics?
I'm new to recommender systems, and I've been reading about how user-based collaborative filtering can group similar users together and (for example) use their ratings to suggest movies to other ...
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49 views
Extracting latent vectors from autoencoder similar to SVD
I have read that there is an equivalency between a linear autoencoder and performing SVD. SVD can be used in collaborative filtering, for example, factorization of a user-movies matrix $\mathbf{M}$ ...
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7 views
Does increasing the number of users improve BPR
I'm trying to improve recommendations for my recommender system using Bayesian personalized ranking. I've tried increasing the amount of seen and unseen items I'm training my model on for a small ...
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5 views
Has there been research on “Hierachical / Adaptive” Context-Aware Recommendation Systems
In many machine learning fields we tend to have "BigDataset", from which we try to extract meaningful signals. In general we attempt to increase valuable data density for an User Interface.
...
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1answer
299 views
Training and testing an autoencoder on very sparsely populated data
I am exploring the possibility of using a deep autoencoder neural net to build a recommender system. I am firstly testing the model's performance on the traditionally used benchmark of the Movielens ...
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1answer
150 views
How to rank products using deep learning for recommender systems?
I am going to implement a recommender system based on this paper.
It basically uses a double embedding technique, one for the user representation and another one for the products (movies, clothes, ...
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1answer
24 views
Which Recommendation Algorithm for Sending Offers to Clients?
We are trying to identify which offers to send to our clients through email. Suppose we have offers with the following data:
Offers:
[
{
id: 1,
countriesVisited: {
...
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141 views
Type of Recommendation engine algorithm to use
I'm planning to build a recommendation engine for a market place. The market place has User1,User2,,,,,,,UserN (with new users joining all the time). Each user has city and state information.
Market ...
3
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1answer
193 views
Autoencoders and Collaborative Filtering: Is one network per training sample really necessary?
I have been researching on how to apply neural networks to recommender systems and have come across this paper (AutoRec: Autoencoders Meet Collaborative Filtering by Sedhain et al.) where they model ...
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24 views
Deep VBPR Regularization Term question
I'm trying to understand/run the code in the repo below:
https://github.com/kang205/DVBPR
It's a tensorflow implementation in python of the model described in this paper:
"Visually-Aware Fashion ...
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18 views
Which curve comparison should I use to evaluate the performance of a recommender?
I am building a recommender system on the Last.FM dataset (link here) (1,892 users and 17,632 artists and the number of times a particular artist was listened to by a user). Next, the raw dataset was ...
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1answer
954 views
What is the relation between SVD and ALS?
I am trying to build a simple CF-recommender system using the small MovieLens data set. In order to do this, I tried to use ALS to factor my (user, item) matrix $A$ into a (user, latent-space) matrix $...
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93 views
How to build a User Profile in Recommendation Systems?
I am building a recommendation system using tf-idf technique and cosine similarity. So far I am already recommending items given an item information. My concern is more about the learning part. I know ...
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29 views
Improving cold-start recommend systems
I am using tensorrec (python framework for recommendation, based on tensorflow) to predict a users choice of content, based on the users meta-data. My current accuracy is at about 2,5% *. Since this ...
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1answer
123 views
Why RMSE over MAE for matrix factorisation?
I have been trying to compare several matrix factorization algorithms and I've noticed that all the papers and libraries I've seen measure the Root Mean Square Error(RMSE) when intuitively I would ...
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73 views
Customer similarity for customer-product interactions over time
I have a table that has 3 columns: Customer, Item and Date of Interaction between customer and item. I would like to calculate a similarity between customers based on how these customers interact with ...
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1answer
412 views
MAE and Precision for Collaborative Filtering Recommender Systems
I have got a question concerning Recommender Systems and Evaluation Metrics. I tested a few collaborative filtering recommendation algorithms on dataset containing amazon ratings.
Here you can see MAE ...
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2answers
70 views
How good is my recommender?
I implemented a recommender using tensorflow, based on e-commerce data. This recommender is predicting the next item a user will buy. I will judge the performance of my fitted model, by getting the ...
