Ranking PapersPapers on recommendation system / search ranking.
Stars: ✭ 29 (-86.45%)
Mt DnnMulti-Task Deep Neural Networks for Natural Language Understanding
Stars: ✭ 1,871 (+774.3%)
RecommendersBest Practices on Recommendation Systems
Stars: ✭ 11,818 (+5422.43%)
intergoA package for interleaving / multileaving ranking generation in go
Stars: ✭ 30 (-85.98%)
image embeddingsUsing efficientnet to provide embeddings for retrieval
Stars: ✭ 107 (-50%)
deno-x-ranking🦕 Deno Third Party Modules Ranking 👑
Stars: ✭ 28 (-86.92%)
ScoreboardStatsBukkit plugin for customizing the sidebar of the scoreboard feature from minecraft
Stars: ✭ 29 (-86.45%)
WhySoMuchknowledge graph recommendation
Stars: ✭ 67 (-68.69%)
EasyRecA framework for large scale recommendation algorithms.
Stars: ✭ 599 (+179.91%)
Multi-task-Conditional-Attention-NetworksA prototype version of our submitted paper: Conversion Prediction Using Multi-task Conditional Attention Networks to Support the Creation of Effective Ad Creatives.
Stars: ✭ 21 (-90.19%)
CondensedMoviesStory-Based Retrieval with Contextual Embeddings. Largest freely available movie video dataset. [ACCV'20]
Stars: ✭ 78 (-63.55%)
BARSTowards open benchmarking for recommender systems https://openbenchmark.github.io/BARS
Stars: ✭ 157 (-26.64%)
JD2Skills-BERT-XMLCCode and Dataset for the Bhola et al. (2020) Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
Stars: ✭ 33 (-84.58%)
iresearchIResearch is a cross-platform, high-performance document oriented search engine library written entirely in C++ with the focus on a pluggability of different ranking/similarity models
Stars: ✭ 121 (-43.46%)
multi-task-learningMulti-task learning smile detection, age and gender classification on GENKI4k, IMDB-Wiki dataset.
Stars: ✭ 154 (-28.04%)
torchMTLA lightweight module for Multi-Task Learning in pytorch.
Stars: ✭ 84 (-60.75%)
temporal-depth-segmentationSource code (train/test) accompanying the paper entitled "Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach" in CVPR 2019 (https://arxiv.org/abs/1903.10764).
Stars: ✭ 20 (-90.65%)
Codechef Cards[Obsolete] WebApp to follow friends doing CP on Codechef platform and to track their ratings and stars.
Stars: ✭ 17 (-92.06%)
mildnetVisual Similarity research at Fynd. Contains code to reproduce 2 of our research papers.
Stars: ✭ 76 (-64.49%)
Mask-YOLOInspired from Mask R-CNN to build a multi-task learning, two-branch architecture: one branch based on YOLOv2 for object detection, the other branch for instance segmentation. Simply tested on Rice and Shapes. MobileNet supported.
Stars: ✭ 100 (-53.27%)
MoTISMobile(iOS) Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP). Accepted at NAACL 2022.
Stars: ✭ 60 (-71.96%)
PCC-NetPCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Stars: ✭ 63 (-70.56%)
salbowSaliency Weighted Convolutional Features for Instance Search
Stars: ✭ 55 (-74.3%)
MHCLNDeep Metric and Hash Code Learning Network for Content Based Retrieval of Remote Sensing Images
Stars: ✭ 30 (-85.98%)
awesome-semantic-searchA curated list of awesome resources related to Semantic Search🔎 and Semantic Similarity tasks.
Stars: ✭ 161 (-24.77%)
AudioAlignAudio Synchronization and Analysis Tool
Stars: ✭ 80 (-62.62%)
cottontaildbCottontail DB is a column store aimed at multimedia retrieval. It allows for classical boolean as well as vector-space retrieval (nearest neighbour search) used in similarity search using a unified data and query model.
Stars: ✭ 16 (-92.52%)
COILNAACL2021 - COIL Contextualized Lexical Retriever
Stars: ✭ 86 (-59.81%)
listenbrainz-labsA collection tools/scripts to explore the ListenBrainz data using Apache Spark.
Stars: ✭ 16 (-92.52%)
Recommendation-system推荐系统资料笔记收录/ Everything about Recommendation System. 专题/书籍/论文/产品/Demo
Stars: ✭ 169 (-21.03%)
cherche📑 Neural Search
Stars: ✭ 196 (-8.41%)
Long-Tail-GANAdversarial learning framework to enhance long-tail recommendation in Neural Collaborative Filtering
Stars: ✭ 19 (-91.12%)
cargo-esrExtended Search & Ranking tool for crates.
Stars: ✭ 23 (-89.25%)
Tf-RecTf-Rec is a python💻 package for building⚒ Recommender Systems. It is built on top of Keras and Tensorflow 2 to utilize GPU Acceleration during training.
Stars: ✭ 18 (-91.59%)
Fine-Grained-or-NotCode release for Your “Flamingo” is My “Bird”: Fine-Grained, or Not (CVPR 2021 Oral)
Stars: ✭ 32 (-85.05%)
FieldedSDMFielded Sequential Dependence Model (code and runs)
Stars: ✭ 32 (-85.05%)
DeepSegmentorA Pytorch implementation of DeepCrack and RoadNet projects.
Stars: ✭ 152 (-28.97%)
hidden-gemsRanking of Steam games which favors "hidden gems". Featured in PC Gamer.
Stars: ✭ 37 (-82.71%)
mtlearnMulti-Task Learning package built with tensorflow 2 (Multi-Gate Mixture of Experts, Cross-Stitch, Ucertainty Weighting)
Stars: ✭ 45 (-78.97%)
toptal-recommenginePrototype recommendation engine built to accompany an article on Toptal Blog
Stars: ✭ 109 (-49.07%)
vitrivr-ngvitrivr NG is a web-based user interface for searching and browsing mixed multimedia collections. It uses cineast as a backend
Stars: ✭ 14 (-93.46%)
go-trueskillAn implementation of the TrueSkill™ ranking system (by Microsoft) in Go
Stars: ✭ 20 (-90.65%)
WSDM2022-PTUPCDRThis is the official implementation of our paper Personalized Transfer of User Preferences for Cross-domain Recommendation (PTUPCDR), which has been accepted by WSDM2022.
Stars: ✭ 65 (-69.63%)
seminarECNU ICA seminar materials
Stars: ✭ 14 (-93.46%)
cs6101The Web IR / NLP Group (WING)'s public reading group at the National University of Singapore.
Stars: ✭ 17 (-92.06%)
TIFUKNNkNN-based next-basket recommendation
Stars: ✭ 38 (-82.24%)
auction-website🏷️ An e-commerce marketplace template. An online auction and shopping website for buying and selling a wide variety of goods and services worldwide.
Stars: ✭ 44 (-79.44%)
UDLFAn Unsupervised Distance Learning Framework for Multimedia Retrieval
Stars: ✭ 40 (-81.31%)
libfmplibfmp - Python package for teaching and learning Fundamentals of Music Processing (FMP)
Stars: ✭ 71 (-66.82%)