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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113 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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2answers
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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1answer
2k views

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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0answers
80 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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1answer
27 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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191 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
274 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
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
806 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
122 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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2answers
169 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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19 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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2answers
170 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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1answer
635 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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1answer
12 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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6 views

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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16 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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73 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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24 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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1answer
200 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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1answer
26 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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36 views

Quiz based recommendation system

For my project I would like to make a 'quiz' based recommendation system (for books for example). If a new user comes to the website I want to find out his taste based on some questions, in which I ...
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1answer
124 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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2answers
438 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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1answer
24 views

Concept drift in in user interaction data

concept drift usually refers to the change in the relationship between input and output data over time. I do have dataset of users' activity in an e-commerce website. Let's say we have a sequence of ...
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1answer
139 views

How to use variables as factors in collaborative filter

I can find many resources on item-item or user-user similarity or collaborative filtering, but am having a hard time finding or knowing which terms to search for when combining them. For example, a ...
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15 views

Can I use learning to rank to recommend products to customers?

I am working on a project to recommend products to customers. I knew the traditional methods was to use recommendation systems. I wonder if I could use learning to rank to provide the best-ranked ...
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2answers
809 views

should one always perform svd before doing KNN?

I am trying to perform a Collaborative filtering for recommendation of products to customers in fashion industry.I am using the usual KNN approach to bring similarities among products. I have seen ...
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2answers
1k 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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1answer
160 views

Recommendation system for news articles?

In the scenario where we recommend movies that a user has not yet watched based on how he/she rated previously watched movies, it seems that: A movie released 5 years ago is [possibly] as good a ...
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1answer
24 views

N-dimension one hot features representation

I was trying to understand the features representation in this paper: DRN: A Deep Reinforcement Learning Framework for News Recommendation In 4.2 Feature construction, the news features is News ...
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1answer
186 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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1answer
146 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
16 views

Training classifier on randomly generated negative samples

I have $M$ (~dozen million) feature vectors. There are $F$ (~several dozen thousand) binary features, but in each vector, only $H$ (~several hundred) of them would be 1, the rest are 0. Now, for a ...
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1answer
629 views

Recommender System + Collaborative filtering without users

So I have a problem where I have a dataset that includes a list of Tools that are tied to Tasks. The data is structured as follows: The users do not rate the Tools, they simply use them in a method ...
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1answer
331 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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1answer
351 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

Recommendation Engine and Text Analytics

I am looking for a dataset on which I can use Collaborative filtering and Content based filtering along with Text Mining. Could anybody please suggest , is there any dataset on which I can apply ...
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16 views

How to validate Item-Based Collaborative Filtering?

So, I made an books recommender system engine (with R) which based on item-item matrix. I've made the whole system fully works the output will give 5 recommender system. But, the question is how can I ...
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26 views

Best Statistical Test for my situation?

For an upcoming project we are trying to create a recommendation algorithm for customers and providers. I will have some number of service providers, probably hundreds to thousands, and some number ...
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0answers
24 views

Teaching movie recommendation network to avoid duplicates

I'm trying to implement a simple movie recommender using a neural network and collaborative filtering, i.e. given a list of movies the user has watched, what is a good movie recommendation. Results ...
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1answer
85 views

FindSimilar items in a complex dataset

Im a MachineLearning newbie, but I want to learn more about this interesting topic using a practical example, on which I would appreciate any theoretical and practical help: I have a database of "...
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1answer
42 views

Overfitting in recommender systems

So I want to know whether or not my models are overfitting or the difference between train and validation errors are decent. $L$: is the number of neighbors The first column is the train error ...
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5answers
37k views

How do I use the SVD in collaborative filtering?

I'm a bit confused with how the SVD is used in collaborative filtering. Suppose I have a social graph, and I build an adjacency matrix from the edges, then take an SVD (let's forget about ...
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0answers
21 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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1answer
1k views

AUC in item recommendation context

I am trying to understand the AUC (Area under the ROC curve) in the context of evaluating the performance of an algorithm for doing item recommendation(e.g. BPRMF). I know how the calculation is ...