Information Retrieval
1119 papers with code • 21 benchmarks • 91 datasets
Information retrieval is the task of ranking a list of documents or search results in response to a query
( Image credit: sudhanshumittal )
Benchmarks
These leaderboards are used to track progress in Information Retrieval
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
MS MARCO
CoNLL 2003
BioASQ
CORD-19
MTEB
QASC
Subtasks
Most implemented papers
Modeling Relational Data with Graph Convolutional Networks
We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
We introduce a new approach to generative data-driven dialogue systems (e. g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
To enable evaluation of progress on code search, we are releasing the CodeSearchNet Corpus and are presenting the CodeSearchNet Challenge, which consists of 99 natural language queries with about 4k expert relevance annotations of likely results from CodeSearchNet Corpus.
Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data
Atomu2014/product-nets-distributed
•
•
User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.
ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
stanford-futuredata/ColBERT
•
•
ColBERT introduces a late interaction architecture that independently encodes the query and the document using BERT and then employs a cheap yet powerful interaction step that models their fine-grained similarity.
Declarative Experimentation in Information Retrieval using PyTerrier
The advent of deep machine learning platforms such as Tensorflow and Pytorch, developed in expressive high-level languages such as Python, have allowed more expressive representations of deep neural network architectures.
Deep Learning based Recommender System: A Survey and New Perspectives
This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems.
Deep Neural Networks for YouTube Recommendations
YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence.
Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
Industrial recommender systems usually consist of the matching stage and the ranking stage, in order to handle the billion-scale of users and items.
Image-based table recognition: data, model, and evaluation
In addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric for table recognition, which more appropriately captures multi-hop cell misalignment and OCR errors than the pre-established metric.
