9. Algoritmy pro řazení (learning to rank) Popište rozdílnost pointwise, pairwise a listwise přístupů
Overview¶
Learning to Rank refers to machine learning techniques applied in the construction of ranking models for information retrieval systems. The algorithms are designed to sort items in a way that is relevant to a user's query or needs. The most popular approaches to Learning to Rank are Pointwise, Pairwise, and Listwise, each having unique properties and handling the ranking problem in distinctive ways.
Pointwise approaches¶
Pointwise approaches look at a single document at a time in the loss function. They essentially take a single document and train a classifier / regressor on it to predict how relevant it is for the current query. The final ranking is achieved by simply sorting the result list by these document scores. For pointwise approaches, the score for each document is independent of the other documents that are in the result list for the query.
All the standard regression and classification algorithms can be directly used for pointwise learning to rank.
Key Points
- Individual Scoring: Each item is scored individually.
- Regression or Classification: Ranking is treated as a regression or classification problem.
- No Relationship Consideration: It doesn't consider the relationships between items.
Pairwise approaches¶
Pairwise approaches look at a pair of documents at a time in the loss function. Given a pair of documents, they try and come up with the optimal ordering for that pair and compare it to the ground truth. The goal for the ranker is to minimize the number of inversions in ranking i.e. cases where the pair of results are in the wrong order relative to the ground truth.
Pairwise approaches work better in practice than pointwise approaches because predicting relative order is closer to the nature of ranking than predicting class label or relevance score.
Key Points
- Pair Comparison: Considers the relative order of a pair of items.
- Minimize Misordered Pairs: Seeks to minimize the number of incorrectly ordered pairs.
- Relative Order: More suitable when the relative order of items is important.
Listwise Approach¶
The listwise approach considers the entire list of items. It directly optimizes the performance metric of interest, often using a listwise loss function. While more complex to implement, it can potentially model the ranking problem more accurately by considering the overall order of items.
Key Points
- Full List Consideration: Considers the entire list of items.
- Direct Optimization: Directly optimizes the performance metric of interest.
- Complex but Accurate: More complex to implement, but has the potential to model the ranking problem more accurately.
Summary:¶
- Pointwise Approach: Treats ranking as a regression or classification problem, and scores each item individually without considering the relationship between items.
- Pairwise Approach: Considers the relative order of a pair of items and seeks to minimize the number of incorrectly ordered pairs.
- Listwise Approach: Considers the entire list of items and directly optimizes the performance metric of interest, often using a listwise loss function.