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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11 views

Is Matrix Factorization also going to work with one feature?

I need to fill missing values. I have found that there are many approaches such as the mean and the median of the feature as well as using Matrix Factorization. However, I am kind of wondering if I ...
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Recommending without knowing the clusters

I've heard of some methods to produce recommendations based on clustering: if many of the users who enjoy A also enjoy B, and you enjoy A, then you are likely to enjoy B. A and B in this case form a "...
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Is there a method to compare prediction from different models?

I have a problem where I need to classify whether a client is likely to buy or not a service, I have around 10 service and 10.000 times more clients than service, and also rules that do not allow ...
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117 views

Compare two datasets and wheather they agree

I have two datasets and they both have the same set of independent variables: 9 of them are on scale from 0 till 100 3 of them are categorical(1 with two types categories, 1 with three types of ...
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1answer
127 views

Recommendation Engine With Physical Distance Cutoff

I am looking to develop a recommendation engine for local stores to users. There are approximately 1 million stores in the database and around 1 million users. The 1Mx1M matrix for a user-based ...
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1answer
13 views

Normalizing product quantities to use them as implicit ratings in Product Recommendation

I am running a product recommendation using ALS method on retail transaction data. A simple question struck my mind on the using the methodology in case of implicit ratings. In my case I am using the ...
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Why to say neural network can extract implicit feature combinations?

I just couldn't understand why to say neural network layers can extract implicit feature interactions in the DeepFM model. What does the keyword, implicit feature interactions, exactly mean here? And ...
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13 views

How to recommend display advertisements to users based on highest click-trough-rate for multiple banners?

I have several ideas and questions but I'm not sure what is the best approach for this. Any help or feedback would be greatly appreciated! Problem description For a company I want to build a ...
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225 views

Why RMSE over MAE for matrix factorisation? [duplicate]

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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1answer
33 views

How does the “age” feature work in video recommendation systems?

In the paper Deep Neural Networks for YouTube Recommendations, it mentions that the “example age” feature helps recommending fresh contents in Section 3.3. Many hours worth of videos are uploaded ...
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473 views

Precision and recall in content-based recommender

I have some trouble understanding the concept of using precision and recall to evaluate a content-based recommender. Suppose I want to recommend articles to users. A content-based recommender will ...
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Context-aware recommender systems for same sets of users

I understand the basic premise of recommendation systems that recommend let's say, products ($X$) to users ($Y$). However, what if the sets $X$ and $Y$ are the same? For example, let's say users have ...
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2k views

How to find nearest neighbors using cosine similarity for all items from a large embeddings matrix?

I have an embeddings matrix of a large no:of items - of around 100k, with each embedding vector length of 100. So a matrix of size 100k x 100; From this, I am trying to get the nearest neighbors for ...
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Proper way to use NDCG@k score for recommendations

Currently I am building a recommender system and using ranking metrics to verify its performance. I am using the NDCG@k score. Today I was experimenting and I realized that I might be calculating the ...
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Recommender System that gives rating and confidence of rating for implicit data

I am trying to find a model that, given an implicit user-item matrix, will return rating estimates for items for a user and some measure of confidence or uncertainty in the score. I am thinking along ...
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12 views

Compare similarity between two different clusters having few similar fields?

I have two datasets, one is mentor dataset another is jobseeker dataset. My ultimate goal is to recommend a jobseeker to mentor based on skills and location (skills jobseeker has but need mentorship ...
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Association Rule Learning to recommend distinct categories---avoiding similar items

I would like to use association rule mining on my orders dataset, however, I want to avoid recommending two similar items. For example, recommending two loaves of bread with butter. I want to ...
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242 views

How to Deal with Categorical Variables that Allow Selection of Multiple Values per Observation?

Say you are dealing with a movie database that has movies and their genres. Genre is a categorical variable but each movie can belong to more than one genre. For example, Movie A may be Comedy and ...
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38 views

A/B testing and Multi-armed bandit algorithms in a recommender system

I was reading about these two algorithms, but I don't understand how they can be used in a recommender system because using the MovieLens dataset these algorithms recommend the best movie for all the ...
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more then one user based Collaborative filtering

I want to get a movie recommendation by selecting multiple users. Usually, it takes one user ID and gives a recommendation. I thought user-based collaborative filtering with KNN is a good idea. I have ...
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1answer
361 views

Selecting the number of hashes for minhash? Working with extremely sparse data and want more collisions

I'm attempting to use minhash to generate clusters and similarities, and I am primarily using ideas from these resources. http://www2007.org/papers/paper570.pdf https://chrisjmccormick.wordpress.com/...
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8 views

How to get recommended users for an item with a matrix factorization recommender system?

Any tutorial or guide I've seen is for recommending items to a user, but how can I recommend users for an item? I am currently using the implicit library alternating least squares model. I have ...
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1answer
156 views

Predicting Multi dependent variables

Data: transaction - product_bought,amount,time,date,Client_age,Client_occupation etc product- 20 types (categorical) If we want to predict by analyzing 1 year of ...
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1answer
37 views

How to interpret recommender scores in implicit matrix factorization?

I am using Alternating Least Squares model from the Implicit library on the LastFM dataset, recommending artists to various users. The input data is simply a sparse matrix of users, artists, number ...
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141 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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8 views

recommendation based on multiple user

I am learning about the recommendation system. How can I make a system where it takes multiple users as input and based on the rating and another attribute it gives recommendation? I have data sets ...
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1answer
338 views

Using cosine similarity to measure similarity between uses is not correct

I have a theoretical question. I have implemented a recommender system using collaborative filtering method. There, I am using cosine similarity method to calculate similarity between two users. I ...
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2answers
119 views

Assumption behind few latent features in recommender systems?

I know in recommender systems you have a rating matrix and then you factorize this matrix into two matrices and then learn those matrices with gradient descent. In those matrices we specify the number ...
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4k views

How to train a LSTM model for a next basket recommendation problem?

I try to use a LSTM model for a problem of next basket recommendation. I would like to apply the same approach as this article in Python using Keras : A Dynamic Recurrent Model for Next Basket ...
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86 views

Row similarity in matrix vs in different factorizations

Suppose an arbitrary $m \times n$ matrix $M$ and the factorizations: Arbitrary: $M = U_a V_a^T$, where $U_a$ is $m \times k$, $V_a$ is $n \times k$ ($k < m,n$), and $rank(U_a)=rank(V_a)=k$. SVD: $...
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28 views

Recommendation system

Hello! In the context of collaborative filtering we must find the elements of the svd matrices that minimize the objective function attached. Since the majority of the actual rankings are missing (...
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204 views

Most efficient way to set up a questionnaire to get to know a user's taste

I have a solid user-item matrix, with which I have build a collaborative filtering recommender system. I also have for each item a number of high quality features. If a new user comes to the website (...
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16 views

How to build a recommender system?

I am starting a new feature for our startup product and we're trying to build a recommender system. Here's an overview of our product: Users are not related to each other (no friends relation ...
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2answers
280 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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1k views

User based Collaborative Filtering (with Python)

I am trying to understand how can I calculate the similarity between userid and itemid. Here is the user-based table.The table ...
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1answer
845 views

How to optimize a SVD recommender regarding number of factors?

I am using R to do recommendation based on pureSVD. It is basically to choose the number of factors and then SVD the user-item matrix and then restore the matrix and provide top-N recommendations for ...
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1answer
15 views

Recommender Engine for documents VS Search engine indexing

I have a lot of books and I want to make recommendations to users based on the description and the title of those books. I think that one way is to preprocess the content of the title and ...
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1answer
127 views

Extra data for similarity matrix in collaborative filtering algorithm

I'm implementing a simple user-based collaborative filtering. So, basically, I use a user vector $U$, and similarity matrix of the items, $H$. But I have extra data about my items, based on which I ...
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218 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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23 views

Ranking metric that takes into account length of result list?

I would like to evaluate a problem where the user select an option from a list of variable length. The task is to provide a ranked list so that the item lower in rank is the most relevant. If I use a ...
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173 views

Android Recommendation App Algorithm

I have this project proposal entitled "Android Based Program Recommendation App". (This application is for those college students who wants to shift to other programs). The app will find a program ...
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767 views

How to build a recommendation system based on customer purchase data instead of standard ratings?

I have learned some basics about using collaborative filtering to build a recommendation system and would like to try it out on a dataset which are a large number of customer purchases in around a few ...
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18 views

How does increasing the rank of latent factor model affect the bias-variance trade off in a recommender system?

I know overfitting means low bias and high variance while under-fitting means high bias and low variance. I want to understand how does increasing the rank of latent factor model affect the bias-...
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Collaborative filtering movie recommender: how to account for missing ratings implying information about user preference?

I'm trying to learn about recommender systems with a fairly standard data set: I have a matrix with thousands of users, thousands of movies, and the ratings that users give to each movie. Obviously, ...
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19 views

What is the standard metric used in recommendation systems to evaluate the rankings?

I was searching for a metric to do this for a while and still could not find. More specifically, my problem is as follows. I have a ranked golden corpus. For example, consider that it looks as ...
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88 views

How can I weight categorical variables to create a user preference score?

I'm working on a collaborative filtering algorithm, possibly paired with content-based similarity, for pairing users with other users. I have plenty of data on users and their like events of other ...
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40 views

Item Based Collaborative Filtering vs Association Rules In Data Mining vs Normalised Co-occurrence Similarity Matrix

Requirement: Find out how similar any two products are, in an e-commerce database, to suggest to the users, similar products, when they've added any product to the cart. Much similar to Amazon's ...
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7 views

What is the difference between SVD and Collabarative filtering precisely?

Both are sometimes used interchangebly but still there is a difference between them .
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10 views

Good way to solve product recommendation

I need to recommend products based on top selling products for a given day in the past. The only independent variable is date which i can derive some information from such as weekday, month etc. The ...
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37 views

Cosine similarity for recommendation systems

Recently picked up recommendation systems and was going through User Based Collaborative Filtering(UB-CF). Somewhere in the text, it specified that cosine similarity is one of the measures to find ...

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