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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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1answer
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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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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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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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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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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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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
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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
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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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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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Build recommendation based on Store history not rating

I am working on recommendation engine which will suggest product/ SKU based on Store purchase history from distributor. I would like to to predict which products their existing stores will use in the ...
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How to find similar users in a social network

I have a set of users from a social network. These users are represented by large sparse vectors. Let's say that a small subset of those users bought a ticket for a particular movie. How could I find ...
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Is each row of latent factors obtained from matrix decomposition (SVD) dependent on the other rows of the higher dimensional matrix?

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 ...
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Is that possible to merge both a rate base and logical base recommendation system?

I am not sure if there exists an answer in previous questions, but I couldn't find. Consider I show pictures to the people and they rate that picture 1-5 if they want to make a detailed rating or if ...
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Deep item-based recommender objective function

I'm trying to understand the following paper written by researchers at eBay that uses deep learning to overcome the problem of making recommendations when you mostly have one-of-a-kind items. A ...
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Evalution Metric for Recommender with one Relevant Document

Suppose I have a bunch of user session data. For each user session, 5 rows are created. Each row contains the user id, item id and whether or not they selected that item. For example : ...
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1answer
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Expected Value of Naive Recommender System

Let $k, n \in \mathbb{N}$, with $k \leq n$. Let $A = (a_1, a_2, ..., a_n)$ be an unordered finite sequence of real numbers. Let $(B_1, B_2, ..., B_k)$ be an unordered sequence of random variables such ...
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Why does Alternating Least Squares (ALS) give us good results for missing values?

I was reading about the alternating least squares algorithm and could follow the math but somehow it didn't click for me. We start with random values for $U$ and $V$ and run the algorithm until we ...
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What type of Neural Network to recommend one of a subset of choices

I hope this counts as on topic, but if it's not I apologise. I have a set of data which is 4 categorical X values, and a set of Y values that are continuous. The X values are drawn from a larger set (...
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Logistic regression vs Recommendor system

I am trying to check my understanding with respect to recommendor systems. Here is my reasoning, option (a) is linear regression/neural net set up while option (c) and (d) appears to be standard ...
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Which properties does the latent space in MF for recommender systems have?

The following is related to Hu, Yifan, Yehuda Koren, and Chris Volinsky. "Collaborative filtering for implicit feedback datasets." 2008 Eighth IEEE International Conference on Data Mining. Ieee, ...
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Location and Constraint aware Question Recommendation to Medical Experts

We have an online platform where users (patients) can ask questions to medical practitioners, after a round of online conversation with practioner they go to the practitioners clinic for further ...
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Can NDCG be used a metric for evaluating implicit feedback based recommender systems?

If we are using binary feedback then Can NDCG be used a metric for evaluating implicit feedback based recommender systems
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difference between factorization machines and collaborative filtering

Can factorization machines be considered as a collaborative filtering method ? If so, do they belong to user-based category or item-based category?
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Stochstic Gradient Descent for Collaborative Filtering

I am currently implementing a model-based Collaborative Filtering approach which relies on the matrix factorization technique. More precisely, I want to factorize the rating matrix ...
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What's the best recommender system for only first-visit recommendations?

I'm trying to build a recommender system that recommends items to users. However, users come only once and need to be accurately recommended on their first and unique visit, thus making cold start the ...
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SVD versus RSVD

In the so-called incremental SVD used for collaborative filtering: http://www.machinelearning.org/proceedings/icml2007/papers/407.pdf http://www2.research.att.com/~volinsky/papers/ieeecomputer.pdf ...
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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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Can labels in a dataset be a vector (or just anything non-primary)?

I am building a recommender system for coding interview questions and want to test this by using what the actual system has recommended. The similar_questions below ...
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How to build a user-profile in a document recommender?

I have a very simple content-based document recommender using TF-IDF and Cosine Similarity. The data is very simple; it has the title and tokenized content. In this scenario, how would I build a user-...
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Why is posterior probability so in BPR paper?

In BPR paper BPR: Bayesian personalized ranking from implicit feedback (Steffen Rendle 2009) $$ \prod_{u \in U} p\left(>_{u} | \Theta\right)=\prod_{(u, i, j) \in U \times I \times I} p\left(i>_{...
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Troubles with Basket-Sensitive Recommendation System using CF + Modified Random Walk

I'm trying to reproduce and understand the "Basket-Sensitive Random Walk" for recommendation systems proposed in the paper "Grocery Shopping Recommendations Based on Basket-Sensitive Random Walk" of ...
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Deriving Multiplicative Update Rules for Regularized NMF

After reading the following CrossValidated post, I cannot derived the correct multiplicative rules for regularized NMF from this paper. They obtain the coefficients $|I_u|$ and $|U_i|$ in the ...
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Cross validation in recommender systems

I am trying to figure this out. I get the idea of how the cross validation works in recommender systems. My question is regarding to the phrase observed ratings, what does it mean? I think it means ...
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How to use likes and dislikes to train a recommendation system

I am working on a user/bank product dataset. For each customer I have the list of products to which he has subscribed and the list of products he has cancelled. I consider that if the customer has ...
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How to convert classification features into a ranking function?

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 classification as follows (i.e. <...