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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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Commonly used metric in NLP literature to compare ranked weighted results with variable importance for top-k results

I have two different search engines that always return the same results but in different orders. The results consist of websites along with confidence scores, which range from 100 to 10,000. The ...
hanugm's user avatar
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how to obtain predictions for all observations in a dataset?

I have a dataset comprising 1864 roads with various traffic and safety features, along with the number of accidents at each road. The objective is to rank these roads based on the number of accidents. ...
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Recommendation System Algorithms for Multi-entity ranking

I'm looking for industry engineering or research papers tacking the problem of universally ranking disparate items. For example, one example is the Doordash recommender, which their team attempted to ...
Matt Harrison's user avatar
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Best model for predicting user ranking of a bag of (travel) options without a fixed size

I need to predict which flights users's will choose, as a %. For example a user searches for flights LHR->JFK. There will be a variable set of available options each with a price, number of legs, ...
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Which python libraries do you recommend for label ranking?

I'm currently looking for python libraries that offer models for the label ranking problem. So provided with a context x and a set of Labels Y, the model should output a ranking of those labels. I'm ...
medislamm123's user avatar
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Ranking risky routes

i'm looking to get the top 10 rank of the most dangerous routes. I have a routes table where each row is a route and it has features such as avg daily traffic for past three years No. of times where ...
Joe's user avatar
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Search recall optimization - what appropriate loss function to use?

I am studying machine learning and wanted to work on a project of my own so that I have better chances after graduating college. I'm studying the application of ML to improve searches using a toy ...
user9343456's user avatar
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1 answer
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Using text embeddings directly to compute similarity vs using them as features for a model that predicts similariy

Say you have a problem where you have a query and a set of result documents and you want to rank the result documents according to the query. Say also you have embeddings for the query and for the ...
user1893354's user avatar
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Is it possible to train an ML model that predicts two different types of variables (numeric, categorial)? Is learning a ranking function possible?

Problem setup: We are given a set of training instances and an algorithm C, composed of two randomized algorithms, A and B. Algorithm A has no hyperparameters. In contrast, B has only one ...
AlgoTriceratops's user avatar
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What are machine learning algorithms which rank films?

Broadly speaking, there are two approaches to producing lists of the best films: human vs machine curation. Human curation involves someone, a critic, making decisions and/or creating a canon, based ...
DJohnson's user avatar
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how to treat tied condition in ndcg calculation?

I am trying to calculate ndcg manually, and not sure how to treat tied condition. For example, item A, B, C, D, the actual rating is 1, 0, 0,0. Should irank = 1,2,3,4. or 1,2,2,2, or 1,3,3,3?
Learning's user avatar
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How to handle using input feature (clicks) when it is used in target too?

I am trying to create a ranking model, where I am thinking about creating ground truth based on clicks by user. But at same time past clicks made by users seems like a vital input feature too. Any ...
dcusmeb's user avatar
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1 answer
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Unsupervised ranking of samples

Say I have a dataset of n samples. I want to maximize every feature’s value. I’m not sure if feature 1 is more important than feature 2, etc. Are there any methods of ranking my samples out there? If ...
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4 votes
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How Does the Reward Model in ChatGPT Calculate Losses?

Reading the InstructGPT paper(which seems to be what ChatGPT was built off of), I found this equation for the reward function. However, I'm struggling to understand how this equation is used to ...
itisyeetimetoday's user avatar
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1 answer
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Combining multiple ranked lists

Suppose I'm given two ranked lists, A and B, with each item in the lists being associated with a score: ...
kevin_was_here's user avatar
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How do I train an ML model that can rank pairs?

Consider that I have a dataset consisting of a list of pairs of the titles of blogposts of the form ...
G. H. Hardly's user avatar
2 votes
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80 views

Smallest possible difference between AUC of two ranker [closed]

If there are 10 positive examples, and 90 negative examples in the test set, what is the smallest possible difference in AUC, between two rankers giving different AUC?
wrek's user avatar
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Optimizing Rank Aggregation of Two Different Methods in Information Retrieval

As the title suggests, I would like to train a rank-aggregating model. My target problem is to rank text2s from a database as best as possible to a given query, <...
You_Donut's user avatar
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Algorithm Comparison for Influencers Ranking

I am working on ranking social influencers on Instagram according to their influential power with the metrics collected below. Metrics collected: username categories (the niche the influencer is in) ...
Lil Ptt's user avatar
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2 answers
541 views

How to interpret rank output from BM25?

I'm using the FTS5 functionality in Sqlite3 to compute a search rank, which uses BM25. Maybe I'm just not seeing it, but I can't find any description of how to actually interpret BM25's output ranking....
Cerin's user avatar
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2 votes
1 answer
329 views

How to determine the "total number of relevant documents" in calculatiion of Recall in Precision and Recall if it's not known? Can it be estimated?

On Wikipedia there is a practical example of calculating Precision and Recall: When a search engine returns 30 pages, only 20 of which are relevant, while failing to return 40 additional relevant ...
Banik's user avatar
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3 votes
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Why is NDCG high even for wrongly ranked predictions?

The NDCG (Normalized Discounted Cumulative Gain) metric for ranking is defined as DCG/IDCG, where IDCG is the ideal DCG and is said to take values in [0, 1]. However, since the DCG will always be ...
Michael's user avatar
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How to ensamble different ranking models?

I have trained two different models, which give a score to each data point. The score of the models it is not necessarily comparable. The score is used to give a ranking, and the performance is ...
Diego Palacios's user avatar
2 votes
1 answer
204 views

Learning to Rank with Unlabelled Dataset

I have folder of about 60k PDF documents that I would like to learn to rank based on queries to surface the most relevant results. The goal is to surface and rank relevant documents, very much like a ...
amber's user avatar
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Na values in rankings

Having a table of rankings containing many Na values, how should I deal with Na values while calculating the correlation between those rankings?
Mir's user avatar
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1 answer
42 views

Calculating statistical ranks between datasets with unpaired observations

The problem is the following: I have multiple datasets for which I want to calculate a ranking for each. All observations contained in the datasets can be arbitrarily permuted, so they are unpaired, ...
Steve's user avatar
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1 answer
74 views

What are the simplest predictive ranking algorithms?

I want to apply a predictive ranking algorithm to a dataset, so I've been reading about Learning to Rank, RankNet and LambdaMART [1][2]. These methods use neural nets or trees as their core framework....
PyRsquared's user avatar
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1 vote
1 answer
106 views

Use dummy variables to create a rank variable. R

I have a series of multiple response (dummy) variables describing causes for a canceled visits. A visit can have multiple reasons for the cancelation. My goal is to create a single mutually exclusive ...
Mar355's user avatar
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1 vote
0 answers
93 views

Which algorithm to use for a very simple ranking problem?

Currently, I have a dataset with 10 features that results in a ranking of 4 items, i.e. [1,2,3,4], [4,3,1,2], [3,2,4,1] or any $4!$ permutations that can arise from the ranking. What algorithms are ...
ppwc's user avatar
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0 votes
1 answer
222 views

Combine the ranking

I have a table to decide what is the top rank subgroup. My idea is to rank independently into each feature (score, quality) then sum the rank to have the final score (see picture below) I am not sure ...
Linh NV's user avatar
1 vote
1 answer
32 views

Changing behaviour of an ML model

I am trying to create a ranking system for recommending books to an user. Let's suppose we have some subjects of books like 'A', 'B', 'C', 'D' and from the past behaviour, it is observed that the user ...
Ricky's user avatar
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6 votes
1 answer
17k views

precision@k and recall@k

Normally, I am familiar with precision and recall evaluation metrics but as you know recall@k and precision@k are different things and used in ranking evaluations especially recommendation systems. I ...
drorhun's user avatar
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1 vote
0 answers
117 views

MongoDB Groupby Rank [closed]

Im Working With Mongodb And Wanted to do a query using Aggregate fucntion. Query Is ...
Noob's user avatar
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0 votes
1 answer
27 views

The upper range of a collected dataset is most likely accurate, but the rest may suffer biased omissions: How to call this phenomenon?

Background: In collecting a dataset of a specific unit ordered by a numeric variable, it is possible that the upper 'cloud' of the dataset is correct, while the 'tail' seems inaccurate. I can thus ...
anpami's user avatar
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1 vote
0 answers
38 views

paired t-test shows no difference between median and Wilcoxon test p value shows that there is a difference between median values ? How to interpret?

I have a dataset. I wanted to do paired t test on it. So I carried out normality test and it showed that it does not follow normal distribution. So I used Wilcoxon test in place of paired t test. The ...
pinky's user avatar
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1 vote
1 answer
372 views

Is there a good alternative to XGBoost for learn to rank?

My problem with XGBoost is that when I load the train dataset into the XGBoost DMatrix, there is a memory spike that is unavoidable, and I can't get my dataset loaded into RAM without crashing first. ...
krissy_fong's user avatar
1 vote
1 answer
63 views

Ranking of feature sets which will be used for binary classification

I have some feature sets say X1 and X2 ... Each feature set have some variable amount of features and there is no intersection between different feature sets Say X1 have 100 features and X2 have 500 ...
kartik khariwal's user avatar
2 votes
1 answer
510 views

Cosine Similarity but with weighting for vector indexes

I am very new to NLP and although this seems like a basic question I don't know how to search for an answer online. This is my problem: I have extracted and ranked keywords from 2 text sources: A ...
K Kreid's user avatar
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2 votes
1 answer
862 views

Ranking problem and imbalanced dataset

I know about the problems that imbalanced dataset will cause when we are working on classification problems. And I know the solution for that including undersampling and oversampling. I have to work ...
Maria's user avatar
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1 vote
1 answer
29 views

What does "freedom" mean in the RankNet paper?

In the RankNet (Learning to Rank using Gradient Descent) paper, paragraph 3.1, it said given the consistency requirements, how much freedom is there to choose the pairwise probabilities? What does ...
Louis Law's user avatar
1 vote
1 answer
179 views

What can help decrease outliers' influence on non-tree models?

I have a feature with all the values between 0 and 1 except few outliers larger than 1. I am trying to collect all the methods that can help to decrease outliers' influence on non-tree models: ...
Revolucion for Monica's user avatar
1 vote
1 answer
27 views

What is the best way to pick the optimized configuration from this dataset?

I have about 8000 configurations in an excel sheet. each configuration has four scores as seen in the image below. I would like to choose the best solution that has the highest lighting level score, ...
Julia_arch's user avatar
1 vote
0 answers
41 views

How to use the position factor in known data as a feature in recommendation surfacing?

The problem is recommending stories on a website, just below each story based on how similar the stories are and some historic data based on what recommended stories were clicked or not clicked. So ...
Della's user avatar
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1 vote
1 answer
35 views

Dataset recommendation, ordinal target variable

Does anyone have any recommendations for datasets with an ordinal target? I'm currently using a rounded target from the Boston dataset. ...
targetXING's user avatar
1 vote
1 answer
637 views

Using BM25 to rank words

How effective is it to use BM25 to rank words, to be more specific i have a dictionary of words and i want to rank only words in a document that are also in my dictionary. I want to rank all words in ...
voltage's user avatar
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0 votes
1 answer
31 views

How to adjust/smooth a certain number using constants or rules

Hi, I am handling a dataset with a customer purchase history. The field ord_cnt represents the purchase without coupon usage, and cpn_ord_cnt represents the purchase with coupon usage. There are two ...
Donsuke's user avatar
1 vote
0 answers
221 views

TF-Ranking transform data to ELWC - ExampleListWithContext

I have read all the guides, videos, and everything, but I have no idea how to convert my feature set to an ELWC datasheet format for TF-Rank ListWise problem. There is no description of this structure....
Gábor Kőrösi's user avatar
1 vote
0 answers
19 views

Recommended papers on Deep Ordinal Regression?

Can people please recommend papers on Deep Ordinal Regression? I'm looking for both the basic papers and the current important state-of-the-art ones - basically, everything that one "must" know in ...
Cheshie's user avatar
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1 vote
1 answer
302 views

How to explain a stable NDCG@K in extreme multilabel recommender model

I am working in a multilabel recommender project and I try to evaluate it as a ranking problem. I calculate recall@k and precision@k which both looks quite well. Recall increases and Precision ...
Tasos's user avatar
  • 3,930
0 votes
1 answer
65 views

Analytic Hierarchy Process - consistency ratio over 50%

I have such data of pair-wise comparison. I calculated consistency ratio for this data of 56%. That's too high for relevant results. Whether is possible to modify initial data to improve consistency ...
Tajni's user avatar
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