# Questions tagged [learning-to-rank]

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### Learning to Rank vs Reinforcement Learning in Information Retrieval - which one is preferable and why?

I am trying to create an information retrieval system which can benefit from user feedback (either implicit, through e.g., click-through data) or explicit (e.g., binary feedback on irrelevant ...
162 views

### How to use ndcg metric for binary relevance

I am working on a ranking problem to predict the right single document based on the user query and use the NDCG metric to measure the model. Given the details : Queries ( Q ), Result Document ( D ),...
120 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 ...
8 views

### Visual/intuitive comparisons between various pairwise loss functions for learning-to-rank tasks

In learning-to-rank, There are various pairwise loss functions such as Bayesian Personalised Ranking (BPR) or Weighted Approximate-Rank Pairwise (WARP), etc. I know that these aim at different ...
12 views

### Solving Feature Distribution variance between Training and Prediction for Ranking models

I am building a linear regression model to improve ranking of documents. And trying to identify problems due to which model performance estimates don't match actual impact One major problem is ...
14 views

### Listwise ranking for query with thousands of docs

Typical listwise ranking model will predict a complete permutation of all docs corresponding to a query q. If the number of docs to one query are large, for example,...
19 views

### Listwise learning to rank with negative sample relevance

Typical listwise learning to rank (L2R) algorithm tries to learn the rank of docs $\{x_i\}_{i=1}^m$ corresponding to a query $q$. If we use correlation efficient to label the relevance between docs ...
13 views

### Ranking based on graph neural network

Can anything point to an example for an implementation of a ranking system which is based on a graph neural network?
45 views

### Multilabel classification for a learning to rank application

I am looking for some suggestions on Learning to Rank method for search engines. I created a dataset with the following data: ...
60 views

### How to perform Learning to Rank for a small dataset

I am very interested in applying Learning to rank to my problem doamin. When I read through the literature of Learning to rank I ...
88 views

### Learning to rank: how is the label calculated?

I am studying learning to rank and not sure I understand how the train sample and final label (relevance score) is constructed. Lets assume we sell furniture online. We have logged customer's query, ...
2k views

### Why does it not need to set test group when using 'rank:pairwise' in xgboost?

I'm new for learning-to-rank. I'm trying to learn the Learning to rank example provided by xgboost. I found that the core code is as follows in rank.py. ...
143 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 ...
247 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 ...
198 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 $n$ ...