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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Recommender System for continuous predictors

I want to build a model that is able to predict the outcome of a user-client interaction. I know that for categorical variables Factorization Machines are a good choice. Imagine for example we are ...
Mirko's user avatar
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The Impact of Vector Magnitudes in Recommendation Systems Matrix Factorization Models

I'm currently exploring latent factor models in recommendation systems, specifically focusing on the interaction between vector magnitudes and the angles between these vectors. While it's clear that ...
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Statistics of algorithm AB testing on occurrences only

I have AB testing in place and I have to compare the data but I have no clue which statistical test to use. I'll try to speak in dating app terms because I think it makes it easier to understand. I ...
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How to re-rank after candidate generation?

I am working on an app recommendation problem. I don't have any app features, but I have user features. I've tried different similarity based models and also using a multiclass classification model's ...
Suraj Nagabhushana Ponnaganti's user avatar
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How to normalize data for weighted sum model

I'm building a simple weighted sum model for ranking. $$ \text{Store Rank} = w_1 \cdot param_1 + w_2 \cdot param_2 \ldots + w_n \cdot param_n $$ The problem here is that one of the parameters depends ...
Ivan's user avatar
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UBCF rating predict for binary data in R package recommenderlab

In the R package recommenderlab's predict function for binary data (specifically unary for market basket analysis), why does the code do the following for the weighted UBCF model? ...
Allan Amorim's user avatar
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RecSys model performance stalling at 47% AUC and F1-Score. Is the problem due to ratio of users to items in my dataset?

I'm having trouble with making my validation metrics go down for the binary_crossentropy and go up for the F1-score and AUC. I've tried tuning my hyper parameters such as the number of latent features ...
Mig Rivera Cueva's user avatar
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Optimization metrics for a single-item recommendation system

I'm working on a recommendation system that's a bit different from ones I've built before. In particular, this system shows only the top item to the user, and the user can either click on it, dismiss ...
Kyranstar's user avatar
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How to choose k for MAP@K?

Scenario: We want to evaluate our recommender system, which recommends items to potential customers when visiting a product detail page. Here are actual relevant items: ...
etang's user avatar
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Co-occurrence matrix factorization ALS

Let's say I have an item-item co-occurrence matrix that I want to factorize. I'll thus only have item factors. Is it possible to learn the factors using ALS ? I can't see how given that we'd have to ...
Koowah's user avatar
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Recommend similar users (instead of items) with collaborative filtering

I'm learning about collaborative filtering, and all the resources I've found so far describes how to find items a user might like. However, how would I find the most similar users to a specific user, ...
Zizheng Tai's user avatar
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Similarity measure for non-binary user preference vector

Cosine similarity can be used to measure the similarity between two vectors that encode the user preferences with the following values: 0: No user feedback (e.g. the user never viewed a merchandise) ...
Zizheng Tai's user avatar
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Measuring perplexity over a limited domain in an LLM

Are there papers/a literature on measuring perplexity in using a Large Language Model such as ChatGPT/Flan over a limited domain? I want to prompt an LLM to do movie recommendations/next job ...
piedpiper's user avatar
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Baseline method for handling popularity bias in recommender system

I am building a recommender system of ads based on a feedforward neural network, where I rank ads by their probability of receiving certain user feedback (e.g. clcik, install, etc). To do so, I use ...
Galo Castillo's user avatar
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Alternating Least Squares for Matrix Factorization with biases

I am attempting to use ALS for matrix factorization, using a loss function that includes user and item biases $c_i$, $d_j$, $$ L = \sum_{(i,j) \in S} (r_{ij} - x_i\cdot y_j^\top - c_i - d_j)^2 + \...
flapinski's user avatar
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Implementing a collaborative filtering model for recommending items to new users and how to evaluate it

I have some data for a collaborative filtering problem, which can be represented in a user-item interaction matrix. The data consists of 20 million orders, where there are 15000 different items, and ...
Jonathan Helgesen's user avatar
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Recommender system - for single user

I am building a recommender system, in which for one system there will be only one user. So we cannot use something like user-user data. Recommendation is an item which contains 10-15 attributes ie ...
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Recommendation Model (students Recommended to a Job opening)

I am currently working on a Recommendation Model. Which takes a Job Openings and Computer Science students and recommend students to the job. Which Model should I use or any suggestions you can give? ...
Hamza Amjad's user avatar
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Is it possible to apply matrix decomposition to a vector, injecting additional information to UV decomposition?

As I am reading about recommender systems in Machine Learning, UV decomposition caught my eye (click for an explanation or see below). So I have two questions: Question 1: what are the drawbacks of ...
Jironymo's user avatar
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Is it ok to have low validation loss from the first epoch?

I'm trying to implement Neural Collaborative Filtering recommender system using Keras, the dataset I'm using is movielens-small. Whatever I do to hyperparameters or network, when training, the ...
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Supervised ranking algorithms

I'm working on a problem statement which involves ranking some short-lived items in an order such that the items expected to sell the most in the next n days are ranked on top - basically ranking ...
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What are the ways to use a LIST of features which are DYNAMIC (contents) in nature?

Any features which is represented as a list of 0 or more elements is what I call a Dynamic feature. Let us suppose an example where there are 10 Million movies and ...
Deshwal's user avatar
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Item based collaborative filtering. Since is an unsupervised task should I do training/test split or not?

I'm learning about recommentation systems and I'm trying to build one using item based collaborative filtering approach. I have this dataset in which the lines correspond the items and columns the ...
Antonio Caipora's user avatar
2 votes
1 answer
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Evaluation of a recommendation system. How can I do that?

I'm learning to make a book recommendation system but I am facing some difficulties to evaluate the model. I chose the collaborative filtering item based strategy. The dataset is a matrix (book, user) ...
Antonio Caipora's user avatar
1 vote
1 answer
269 views

When adding batch norm layer do I need to added to all layers in DNN?

While developing deepfm model network I want to add batch norm layer because model seems to suffer from vanishing gradient. There are embedding layers, 2 layers a in deep model part and one dense ...
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When the accuracy curve is U-shaped

I am currently working on MLP-based recommendation system. During training, the model updates based on BCE loss function with train set, then shows the hit rate (rate of how ground truth item is in ...
esh3390's user avatar
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6 votes
2 answers
785 views

ALS vs SGD in parallelization

So given the standard objective in matrix factorization for collaborative filtering of minimizing: $$ L = \sum_{u,i \in S} (r_{ui}-q_i^Tp_u)^2 + \lambda(\sum_i||q_i^2||+\sum_u||p_u^2||) $$ , where $r_{...
wwyws's user avatar
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Recommender system with unique products

Task definition I've been tasked to build a recommender system and I have to admit I'm a beginner in this field. Entities in my system are buyers and unique products that could be bought. You could ...
skywall's user avatar
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Predictions based on data with correlations within and between multiple sets of time series

I'm looking for a model to learn relationships within and between a set of partially observed time series in order to generate predictions for any timepoint in any of the set of time series. More ...
jogall's user avatar
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How to evaluate the performance of recommender systems without having labeled data

I have a huge citation graph of research papers and datasets. So, there is an edge among two items when one of them cites another. So far I've used Node2Vec for creating a dataset recommender system ...
Morteza's user avatar
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Content Based Recommendation System

how will a content-based recommendation system recommend after a sudden change in taste of user?? what will the system recommend to new users without any data about them available in content-based ...
Vivek Pawar's user avatar
3 votes
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45 views

asking humans to rank items

I have around 50 items and I need to ask human graders to rank them. Is there any good resource on how to design this crowd sourcing task? For example, it will be tiresome to ask humans to rank 50 ...
A.M.'s user avatar
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Recommender System based on item-item features evaluation method

The idea is to recommend books purely on the characteristics of it without any user's input (ratings) only getting top N books that are the most similar to a book. I am implementing Euclidean distance ...
Karen Nino's user avatar
2 votes
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119 views

Transition matrix as a feature to feed machine learning algorithm

Currently, I am trying to replicate a paper to extend the research on the paper they create a transition matrix similar to this one: my question is : how can I feed this information into my KNN or ...
user359832's user avatar
3 votes
1 answer
287 views

Matrix Factorization and Overfitting

I recently came accross the algorithm of Matrix Factorization for a recommendations system. One of the tutorials I followed can be found here. According to it given the initial matrix $R$ and the ...
RookieCookie's user avatar
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79 views

How to choose the best recommender system? What evaluation metrics to use?

I want to build a recommender system to suggest similar songs to continue a playlist (similar to what Spotify does by recommending similar songs at the end of a playlist). I want to build two models: ...
Pybubb's user avatar
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1 answer
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Recommend items to complete a set

I'm trying to predict/recommend items to add to an incomplete basket. In this case, the basket is a project — e.g., you detect a customer trying to fix the AC in their car, hence you suggest things ...
misterte's user avatar
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1 answer
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weighted average in collaborative filtering

I studied on collaborative filtering recently and found that for ratings at last most methods applied were weighted average, no matter what they had proposed, similarity, time, etc. Thus I am ...
Maul Seil's user avatar
1 vote
1 answer
51 views

What is the general right call to make from a marginal difference in A/B test results in recommender system?

This was one of the business-related questions from my technical interview last week for a data science position in a recommender system team at a search engine company focusing on advertisement ...
jstaxlin's user avatar
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159 views

Statistical test for recommendation algorithms

I've developed 2 different image based recommendation systems for an e-commerce company and now it's time to evaluate them using some statistical tests. Both algorithms work in a way in which if fed ...
Parseval's user avatar
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2 votes
1 answer
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Sample size calculation for precision at $k$: a/b-testing a recommender system

I would like to conduct an online experiment to compare two different versions of a recommender system. The system returns a list of $r$ ranked recommendations. I would like to evaluate the ...
MightyCurious's user avatar
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511 views

Funk SVD for binary data - product like or dislike

Assume the following situation: you have a user-item sparse matrix. However, instead of the usual 1 to 5 rating scale, items can only receive a positive (1) or negative (-1) feedback. Thus, the matrix ...
wtfzambo's user avatar
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1 vote
0 answers
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Content-based filtering recommendation system for a group

Is there any example of content-based filtering recommendation for a group of observations? For example, user can choose a group of 10 movies they like the most, and the recommendation engine will ...
badatstats's user avatar
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1 answer
210 views

Sequential recommendation: how to effective encoding output item?

Now I am learning about sequential recommendation - session based recommendation. I have understood that User-item interactions may be viewed as sequential action (first I clicked item A, then click ...
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1 answer
515 views

Is there one splitting strategy for both K-NN and Matrix Factorization recommender systems?

I am researching several different recommender systems, some of which are based on a user K-Nearest Neighbour algorithm and some of which are based on a matrix factorization algorithm. My dataset is ...
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1 vote
1 answer
733 views

What's the difference between a recommender system and a decision tree?

We learned in Machine Learning that both of those techniques try to predict an output (whether person A likes a specific product or whether person A has a high default risk) based on data of other ...
StatisticsNoobie's user avatar
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How many variables for a recommender system?

I'm creating a survey for 60 people. I'd like to measure their music, tv-series and purchasing tastes. The survey will show a number of songs. People will be asked to say if they like it (1) or not (0)...
vincenzojrs's user avatar
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451 views

Next best offer models in recommendation system

I'm trying to so some research on "Next Best Offer" or "Next Best Action" models used in recommendation systems. Searched on google but didn't find any article with detailed ...
Cherry Wu's user avatar
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1 answer
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Search, rank and recommend in large text datasets

Imagine you are Spotify and you have billions of songs. Assume that each of these songs are transcribed into text. How do you design your search and recommendation pipeline such that when somebody ...
mhsnk's user avatar
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Constructing a User Profile for Music Taste

My goal is to construct User profiles based on positive (and maybe also negative) interactions with songs. A User has the option to like a song. This would give me a list of likes for each user. With ...
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