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.
MovieLens 1M
SSE-PT
MovieLens 20M
HyperML
MovieLens 100K
GHRS
MovieLens 10M
scaled-CER
Amazon-Book
HSTU+MoL
Gowalla
NESCL
Netflix
H+Vamp Gated
Yelp2018
NESCL
Douban Monti
GLocal-K
ReDial
KERL
Douban
I-CFN
Million Song Dataset
EASE
Flixster Monti
IGMC
Amazon Beauty
ProxyRCA
Amazon Games
CARCA
YahooMusic Monti
MG-GAT
Flixster
TransCF
Amazon Fashion
SAERS
Epinions
DANSER
YahooMusic
GRALS
Amazon Men
CARCA Learnt + Con
Last.FM
Ekar*
Polyvore
Fashion GAE
Amazon-CDs
HGN
Amazon Product Data
TLSAN
Book-Crossing
KGNN-LS
DBbook2014
KTUP (soft)
Frappe
INN
WeChat
DANSER
Yelp
ConvNCF
Alibaba-iFashion
HAKG
Amazon-Beauty
LT-OCF
Amazon Books
Multi-Gradient Descent
Amazon C&A
Amazon-Electronics
Amazon-Health
Amazon-Movies
HetroFair
BeerAdvocate
CFM
Ciao
CiteULike
Declicious
TransCF
Delicious
Dianping-Food
KGNN-LS
Echonest
Epinions-Extend
Fashion-Similar
SR-PredAO(SGNN-HN)
GoodReads-Children
HGN
GoodReads-Comics
HGN
Last.FM-360k
MovieLens-Latest
RATE-CSE
Pinterest
PixelRec
SASRec
Steam
SASRec
Tradesy