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Found 16 papers, 6 papers with code
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling
The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions.
LightningNet: Distributed Graph-based Cellular Network Performance Forecasting for the Edge
The cellular network plays a pivotal role in providing Internet access, since it is the only global-scale infrastructure with ubiquitous mobility support.
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Distributed (or Federated) learning enables users to train machine learning models on their very own devices, while they share only the gradients of their models usually in a differentially private way (utility loss).
Supervised Advantage Actor-Critic for Recommender Systems
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
Multi-Objective Reinforcement Learning
reinforcement-learning
+2
Graph Convolutional Embeddings for Recommender Systems
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking
This paper aims to stir debate about a disconcerting privacy issue on web browsing that could easily emerge because of unethical practices and uncontrolled use of technology.
Impact of Response Latency on User Behaviour in Mobile Web Search
Traditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval community.
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP.
Self-Supervised Reinforcement Learning for Recommender Systems
A major component of RL approaches is to train the agent through interactions with the environment.
Learning Efficient Representations of Mouse Movements to Predict User Attention
Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs.
Graph Highway Networks
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.
A Price-Per-Attention Auction Scheme Using Mouse Cursor Information
There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed.
A Simple Convolutional Generative Network for Next Item Recommendation
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.
