Self-supervised learning for recommender systems: A survey
In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …
tremendous success, but they still fall short of expectation when dealing with highly sparse …
Map: A model-agnostic pretraining framework for click-through rate prediction
With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …
prediction has received more and more attention and research. The most prominent features …
A comprehensive survey on self-supervised learning for recommendation
Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …
by delivering personalized recommendations based on individual user preferences. Deep …
Cl4ctr: A contrastive learning framework for ctr prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced
architectures to model complex feature interactions but neglected the importance of feature …
architectures to model complex feature interactions but neglected the importance of feature …
Contrastive self-supervised learning in recommender systems: A survey
Deep learning-based recommender systems have achieved remarkable success in recent
years. However, these methods usually heavily rely on labeled data (ie, user-item …
years. However, these methods usually heavily rely on labeled data (ie, user-item …
Towards deeper, lighter and interpretable cross network for ctr prediction
Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …
online advertising. It is crucial to effectively model feature interactions to improve the …
A survey on user behavior modeling in recommender systems
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been
extensively used in recommender systems. Crucial interactive patterns between users and …
extensively used in recommender systems. Crucial interactive patterns between users and …
Intent Contrastive Learning with Cross Subsequences for Sequential Recommendation
The user purchase behaviors are mainly influenced by their intentions (eg, buying clothes
for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can …
for decoration, buying brushes for painting, etc.). Modeling a user's latent intention can …
On-device integrated re-ranking with heterogeneous behavior modeling
As an emerging field driven by industrial applications, integrated re-ranking combines lists
from upstream sources into a single list, and presents it to the user. The quality of integrated …
from upstream sources into a single list, and presents it to the user. The quality of integrated …
DFFM: Domain Facilitated Feature Modeling for CTR Prediction
CTR prediction is critical to industrial recommender systems. Recently, with the growth of
business domains in enterprises, much attention has been focused on the multi-domain …
business domains in enterprises, much attention has been focused on the multi-domain …