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FinalMLP: an enhanced two-stream MLP model for CTR prediction
Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …
Final: Factorized interaction layer for ctr prediction
Multi-layer perceptron (MLP) serves as a core component in many deep models for click-
through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning …
through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning …
Rechorus2. 0: A modular and task-flexible recommendation library
With the applications of recommendation systems rapidly expanding, an increasing number
of studies have focused on every aspect of recommender systems with different data inputs …
of studies have focused on every aspect of recommender systems with different data inputs …
MAInt: A multi-task learning model with automatic feature interaction learning for personalized recommendations
Nowadays, more and more recommender systems apply multi-task learning to provide users
with more accurate personalized recommendations. However, most of the existing multi-task …
with more accurate personalized recommendations. However, most of the existing multi-task …
Triangle graph interest network for click-through rate prediction
W Jiang, Y Jiao, Q Wang, C Liang, L Guo… - Proceedings of the …, 2022 - dl.acm.org
Click-through rate prediction is a critical task in online advertising. Currently, many existing
methods attempt to extract user potential interests from historical click behavior sequences …
methods attempt to extract user potential interests from historical click behavior sequences …
Ps-sa: an efficient self-attention via progressive sampling for user behavior sequence modeling
As the self-attention mechanism offers powerful capabilities for capturing sequential
relationships, it has become increasingly popular to use it for modeling user behavior …
relationships, it has become increasingly popular to use it for modeling user behavior …
GPRec: Bi-level User Modeling for Deep Recommenders
GPRec explicitly categorizes users into groups in a learnable manner and aligns them with
corresponding group embeddings. We design the dual group embedding space to offer a …
corresponding group embeddings. We design the dual group embedding space to offer a …
Bayesian attention‐based user behaviour modelling for click‐through rate prediction
Exploiting the hierarchical dependence behind user behaviour is critical for click‐through
rate (CRT) prediction in recommender systems. Existing methods apply attention …
rate (CRT) prediction in recommender systems. Existing methods apply attention …
Dynamic explicit embedding representation for numerical features in deep ctr prediction
Y Cheng - Proceedings of the 31st ACM International Conference …, 2022 - dl.acm.org
Click-Through Rate (CTR) prediction is a key problem in web search, recommendation
systems, and online advertising display. Deep CTR models have achieved good …
systems, and online advertising display. Deep CTR models have achieved good …
Re-sort: Removing spurious correlation in multilevel interaction for ctr prediction
Click-through rate (CTR) prediction is a critical task in recommendation systems, serving as
the ultimate filtering step to sort items for a user. Most recent cutting-edge methods primarily …
the ultimate filtering step to sort items for a user. Most recent cutting-edge methods primarily …