Recommendation Systems
推荐系统(Recommendation Systems)是一种利用用户行为数据、偏好信息和物品特征,通过算法模型预测用户对物品的兴趣度,从而提供个性化推荐的技术。其核心目标是优化用户体验,提高用户满意度和平台黏性,同时增加业务转化率和收入。推荐系统广泛应用于电子商务、社交媒体、在线视频和音乐流媒体等领域,通过精准匹配用户需求与平台资源,实现高效的信息过滤和价值传递。
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