Adamerging: Adaptive model merging for multi-task learning
Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.
A recent development known as task arithmetic has revealed that several models, each fine …
A recent development known as task arithmetic has revealed that several models, each fine …
Contrastive Learning and Deep Fusion Recommendation Model based on ID Features
B Li, X Wang, J Dong, Y Hou, B Yang - IEEE Access, 2024 - ieeexplore.ieee.org
In recent years, the application of deep learning in recommendation systems has achieved
breakthrough progress. Neural networks have captured the complex nonlinear relationships …
breakthrough progress. Neural networks have captured the complex nonlinear relationships …
Representation Surgery for Multi-Task Model Merging
Multi-task learning (MTL) compresses the information from multiple tasks into a unified
backbone to improve computational efficiency and generalization. Recent work directly …
backbone to improve computational efficiency and generalization. Recent work directly …
Deconfounding User Preference in Recommendation Systems through Implicit and Explicit Feedback
Recommender systems are influenced by many confounding factors (ie, confounders) which
result in various biases (eg, popularity biases) and inaccurate user preference. Existing …
result in various biases (eg, popularity biases) and inaccurate user preference. Existing …
Collaborative Knowledge Fusion: A Novel Approach for Multi-task Recommender Systems via LLMs
Owing to the impressive general intelligence of large language models (LLMs), there has
been a growing trend to integrate them into recommender systems to gain a more profound …
been a growing trend to integrate them into recommender systems to gain a more profound …
LEADRE: Multi-Faceted Knowledge Enhanced LLM Empowered Display Advertisement Recommender System
Display advertising provides significant value to advertisers, publishers, and users.
Traditional display advertising systems utilize a multi-stage architecture consisting of …
Traditional display advertising systems utilize a multi-stage architecture consisting of …
Feature Interaction Fusion Self-Distillation Network For CTR Prediction
L Sang, Q Ru, H Li, Y Zhang, Q Cao, X Wu - arxiv preprint arxiv …, 2024 - arxiv.org
Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online
advertising, and search engines. Most of the current approaches model feature interactions …
advertising, and search engines. Most of the current approaches model feature interactions …
NeSHFS: Neighborhood Search with Heuristic-based Feature Selection for Click-Through Rate Prediction
Click-through-rate (CTR) prediction plays an important role in online advertising and ad
recommender systems. In the past decade, maximizing CTR has been the main focus of …
recommender systems. In the past decade, maximizing CTR has been the main focus of …