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Everything about Relevance feedback
Data used in information retrieval and recommendation systems
Relevance feedback is a feature of some information retrieval and recommender systems. The idea behind relevance feedback is to take the results that are initially returned from a given query, to gather user feedback, and to use information about whether or not those results are relevant to perform a new query. We can usefully distinguish between three types of feedback: explicit feedback, implicit feedback, and blind or "pseudo" feedback. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Relevance feedbackA 5-minute orientation
- Skim a recent paper: Designing a new deep convolutional neural network for content-based image retrieval with relevance feedbackInfluential work since 2022
- Pick up a classic book: A generative theory of relevanceThe most-reprinted book on the subject
- Read the landmark paper: Relevance feedback in image retrieval: A comprehensive reviewCited 616 times
- Go sideways: Query expansionA closely related topic
- Go sideways: Relevance (information retrieval)A closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Relevance feedback in image retrieval: A comprehensive review
Improving retrieval performance by relevance feedback
Negative Samples Analysis in Relevance Feedback
Relevance Feedback in Region-Based Image Retrieval
Semantic-oriented 3d shape retrieval using relevance feedback
Flexible pseudo-relevance feedback via selective sampling
Incorporating user search behavior into relevance feedback
Interactive Content-Based Image Retrieval Using Relevance Feedback
🆕 Influential since 2022
Designing a new deep convolutional neural network for content-based image retrieval with relevance feedback
A probabilistic framework for integrating sentence-level semantics via BERT into pseudo-relevance feedback
ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval
The psychology of likes: Relevance of feedback on Instagram and relationship to self-esteem and social status.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
📚 Books worth reading
A generative theory of relevanceVictor Lavrenko · 2008
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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