Recommendation Systems
A recommendation system is a technology that leverages user behavior data, preference information, and item features to predict users' interest in items through algorithmic models. Its core objective is to optimize the user experience, enhance user satisfaction and platform stickiness, while also increasing business conversion rates and revenue. Recommendation systems are widely used in e-commerce, social media, online video, and music streaming platforms, among others, to effectively match user needs with platform resources, achieving efficient information filtering and value delivery.
Alibaba-iFashion
HAKG
Amazon Beauty
ProxyRCA
Amazon-Beauty
Amazon-Book
HSTU+MoL
Amazon Books
Multi-Gradient Descent
Amazon C&A
Amazon-CDs
HGN
Amazon-Electronics
Amazon Fashion
ProxyRCA
Amazon Games
CARCA
Amazon-Health
Amazon Men
CARCA Learnt + Con
Amazon-Movies
HetroFair
Amazon Product Data
TLSAN
BeerAdvocate
CFM
Book-Crossing
KGNN-LS
Ciao
CiteULike
DBbook2014
KTUP (soft)
Declicious
TransCF
Delicious
Dianping-Food
KGNN-LS
Douban
∞-AE
Douban Monti
GLocal-K
Echonest
Epinions
DANSER
Epinions-Extend
Fashion-Similar
SR-PredAO(SGNN-HN)
Flixster
TransCF
Flixster Monti
IGMC
Frappe
INN
GoodReads-Children
HGN
GoodReads-Comics
HGN
Gowalla
NESCL
Last.FM
Ekar*
Last.FM-360k
Million Song Dataset
EASE
MovieLens 100K
FedPerGNN
MovieLens 10M
scaled-CER
MovieLens 1M
GLocal-K
MovieLens 20M
HSTU
MovieLens-Latest
RATE-CSE
Netflix
H+Vamp Gated
Pinterest
PixelRec
SASRec
Polyvore
NGNN
ReDial
KERL
Steam
SASRec
Tradesy
WeChat
DANSER
YahooMusic
GRALS
YahooMusic Monti
MG-GAT
Yelp
ConvNCF
Yelp2018
SVD-AE
LT-OCF