Questions tagged [ranking]

Ranking is ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the "valuation" tag, too.

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15 views

Compare Rows Within a Group and Rank Best to Worst

I'm interested in an approach for comparing rows "within group" to produce a ranking from best to worst based on the performance relative to other rows within a group. For example, if given this ...
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Any there databases with native support for applying a NN model to produce search rankings?

The Situation: I have a simple neural net with an input vector that consists of the euclidean distance between the attributes of two cars. (For example the attributes would include wheel size, car ...
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How to adjust ranking function

I have a ranking function that ranks particular markets in a way where their features are highly desirable. However, I can look at the results of the rank and see that it's good, but how can I make it ...
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11 views

PageRank computation with dumping (scaling) factor

it is not clear to me how to calculate two iterations of PageRank computation on the following network with dumping (scaling) factor s = 1.how can they calculate it correctly?
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1answer
33 views

Calculate a ranking function from classification features

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 ...
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Personalised search ranking for hotels

I've built hotel embeddings which gives very satisfactory results in returning similar hotels for each hotel. Now the problem I'm trying to solve is to rank the hotels in order of relevancy to the ...
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1answer
190 views

Improve results using user input

I've developed a tool that retrieve the closest expressions from a database based on what the user typed. (using word embedding - a comparison is made between each expression from the database and the ...
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57 views

What does the ratio of precision/recall to their ideal values mean, and why are they equal?

When evaluating rankings, Normalized Discounted Cumulative Gain (NDCG) normalizes the score to a [0,1] range by dividing by the ideal score. What happens if we take the same idea to Precision (Pre) ...
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203 views

Xgboost rank:ndcg learning per group or for all dataset

I'm trying to implement xgboost with an objective of rank:ndcg I want the target to be between 0-3. In my data for most of the groups, there is only 1 event per ...
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35 views

What is the difference between nDCG and rank correlation methods?

When do we use one or the other? My use case: I want to evaluate a linear space to see how good retrieval results are. I have a set of data X (m x n) and some weights W (m x 1). I want to measure ...
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41 views

Feature selection with information gain (KL divergence) and mutual information yields different results

I'm comparing different techniques for feature selection / feature ranking. Two of the techniques under scrutiny are the mutual information (MI) and the information gain (IG) as used in decision trees,...
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Item item similarity from both user data and content

I'm creating an item-item similarity matrix by combining implicit user data and static content data of the items. How can I determine the weights for these two data and the weights for different ...
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41 views

Constructing a graph where any node can have the highest PageRank among all nodes

I'm trying to solve q5.1.4 in mmds chapter5, however, I'm not sure how I can even start. The question is: Construct, for any integer n, a Web such that, depending on β, any of the n nodes can have ...
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1answer
58 views

Multilabel Classification With Ranking

I have a dataset as below: ...
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1answer
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Best way to narrow down a list and rank based on attributes?

I have a mortgage/credit data set that contains a list of customers (600k rows) and has a 100 columns inclusive of the customer's general info (address, city, zipcode, etc), income, fico scores, ...
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1answer
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Predicting sequences newbie question [closed]

I have a ranked list of rows of 100 lines of data 1- 8 4 0 5 9 3 2- 0 3 3 5 3 2 3- 0 0 2 4 0 2 .. 100- 0 2 3 2 2 0 Is it possible to predict a) when given a new ...
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107 views

NDCG score is greater than 1

I'm solving a problem of ranking classes for each unique id based on the utilization quantity. I have 6 unique classes in the training and test data. My neural net mode predicts the utilization ...
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28 views

Reduce number of vectors in dataset to achieve the “same average dimensions result”?

I have many tests (rows), each with a large set of 3D vectors (features/cols). Each vector complies: Xn + Yn + Zn = 1 Simply averaging all components ...
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1answer
118 views

Two definitions of DCG measure

I wanted to check the definition of Discounted Cumulative Gain (DCG) measure in the original paper Jarvelin and it seems it differs from the one given in the later literature Wang. Originally, for $n$ ...
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82 views

Statistical Significance in Pairwise Ranking Algorithm

Can anyone recommend an algorithm/toolkit to rank items that have been rated in a hot-or-not style that gives statistical significance? For example, out of a set of N images, two images are shown to ...
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138 views

Statistical Significance for Pairwise Ranking Algorithm

I am using this library: https://github.com/lucasmaystre/choix/blob/master/notebooks/intro-pairwise.ipynb to rank items that have been rated in a hot-or-not style. For example, two images are shown ...
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209 views

Clarification about Normalized Discounted Cumulative Gain (NDCG) together with Regression for Ranking?

I want to rank products from 1 to M, my dataset looks like this: ...
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1answer
74 views

Sparse IR with user feedback

I'm considering a problem framing within an information retrieval context. I have a sequence of documents that feature different attributes. In the web context, these would be webpages. One ...
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2answers
95 views

Learning to Rank Application

If there's a website/app that sells products and my job is to determine the order/ranking in which the products should be displayed. For example : I click on restaurants and a list of restaurants ...
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10 views

business ranking based on transaction data

I have some customer (say 10,000 customers) transaction data of many stores, what would be a good approach to rank these stores? The transaction data is of entire year, and the transaction data ...
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1answer
91 views

Methods for ensembling ranked lists?

I was wondering if there's a good way to use ensembling when I have two or more algoritims producing ranked lists. That is, suppose I have the following datasets consisting of ordered lists (higher ...
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1answer
463 views

Can feature importance change a lot between models?

I have a random forest classifier and Multinomial Naive Bayes. For feature importance, I used gini index for random forest and for Multinomial Naive Bayes I used the coefficients of each feature. Then ...
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1answer
95 views

Peformance evaluation of ranking algorithms

I have three questions: How can we assess (or measure) the performance of the ranking algorithms? Are there any specific measures, or performance metrics, for this? More specifically, how can we ...
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2answers
732 views

XGBoost ranking file format

The xgboost package has two files that must be used for ranking: train.txt with the data ...
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1answer
3k views

Cosine similarity between query and document confusion

I am going through the Manning book for Information retrieval. Currently I am at the part about cosine similarity. One thing is not clear for me. Let's say that I have the tf idf vectors for the ...
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59 views

Information measure of rank changes?

I'm trying to compute the information content of a function that reranks a list. Perhaps more precisely, I'm trying to compare the information content of different arguments to that function. ...
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1answer
62 views

Model with rank as the dependent variable?

I have a dataset with n records. The input features are f1, f2, f3 ... fm, and the dependent variable (output) is just the rank (not any type of scores). What type of model should I build so that I ...
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1answer
115 views

Learning to rank: construct absolute ranking using pair-wise ranking approach

I am learning about the "pairwise approach" for learning to rank. As far as I understood, the training output is a partial ranking function $r$ that: given given some query $q$ and two document $d_i$ ...
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0answers
330 views

Ranking algorithm based on a handful of features

I am trying to determine the apt algorithm for a ranking problem that I am working on. I have social media metrics - engagement, sentiment, audience size etc for several brands and am looking for a ...
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0answers
358 views

xgboost - How do I treat document ID in pairwise ranking

I am trying to use xgboost in R for pairwise ranking for an implicit dataset. For simplicity, let's assume that I am dealing with a search problem, where I want to rank documents relative to a given ...
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1answer
45 views

Rating the Models

I am working with Multi variate time series Analysis using different Models in R. I used Arima, glm, ...
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0answers
329 views

Learning ranking

This is a sort of a follow-up to this newbie question:Suppose I want learn ranking (so, I have a bunch of data points, ranked $1, 2, 3, ...$ Now, one way is to use something like logistic regression ...
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1answer
62 views

Learning comparisons

Suppose I want (the machine) to learn the ">" function, and my training set is a collection of pairs of numbers $(n_1, n_2)$ with the output True if $n_1 > n_2$ ...
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4answers
5k views

From pairwise comparisons to ranking - python

I have to solve a ranking ML issue. To start with, I have successfully applied the pointwise ranking approach. Now, I'm playing around with pairwise ranking algorithms. I've created the pairwise ...
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1answer
24 views

What algorithm would I use to create a grade based on 3 numeric attributes?

I'm currently using Weka to prototype some possible applications for machine learning. One such application is a grade based on the performance of a user's mailing list. I have the following ...
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2answers
394 views

Rank feature selection over multiple datasets

Through backward elimination I get a ranking of features over multiple datasets. For example in the dataset 1 I have the following ranking, the feature in the top being the most important: feat. 1 ...
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5answers
3k views

Machine learning algorithm for ranking

I am working on a ranking question, recommending k out of m items to the users. The evaluation metric is average precision at K. Both R and Python have xgboost can be used for pairwise comparison ...
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2answers
146 views

Use regression instead of classification for hard labeled ranking datasets

Let's imagine I have a dataset of movie reviews with annotated sentiment: -1 means negative 0 means neutral +1 means positive I see a lot of people trying to do ...
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1answer
39 views

Given rankings and the factors that determined the rankings, what is the best method to reverse engineer the rankings?

I have a list of ranked items and the factors that the group used to create the rankings (like college rankings: 1, 2, 3...), and I'd like to reverse engineer their methodology. Here's what I tried......
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2answers
1k views

Converting non-numeric data values into equivalent rank scores

Consider a data-frame similar to the one shown (the actual data-frame is much larger) ...
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2answers
321 views

Algorithm Suggestion For a Specific Problem

I'm working on a problem where in I have some data sets about some power generating units. Each of these units have been activated to run in the past and while activation, some units went into some ...
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252 views

Multi-label ranking

I have some input data which can belong to different outputs. I'm trying to determine if there's a pattern between the inputs and the output. I'm exploring using multi-label support vector machines in ...
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67 views

What's the best way to rank aggregate imdb rating data?

I have an average rating of all votes as well as the total number of votes for all episodes of the TV show Always Sunny. Is it mathematically sound to?: 1) multiply each average rating by the total ...
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2answers
12k views

How fit pairwise ranking models in xgBoost?

As far as I know, to train learning to rank models, you need to have three things in the dataset: label or relevance group or query id feature vector For example, the Microsoft Learning to Rank ...
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1answer
2k views

What are the top 10 problems yet to solve in machine learning? [closed]

Can somebody answer that? It would be good if the answer comes with evidences or some research paper. I'm not asking for opinions