45 citations · 169 across the 14 of their papers we have counts for
15 papers
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFT
Junchen Fu, Xuri Ge, Xin Xin +4
Multimodal foundation models are transformative in sequential recommender systems, leveraging powerful representation learning capabilities. While Parameter-efficient Fine-tuning (…
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling
Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis +1
Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations fr…
P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups
Ioannis Arapakis, Panagiotis Papadopoulos, Kleomenis Katevas +1
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 diffe…
Supervised Advantage Actor-Critic for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
Casting session-based or sequential recommendation as reinforcement learning (RL) through reward signals is a promising research direction towards recommender systems (RS) that max…
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning
Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis +2
Since the inception of Recommender Systems (RS), the accuracy of the recommendations in terms of relevance has been the golden criterion for evaluating the quality of RS algorithms…
Graph Convolutional Embeddings for Recommender Systems
Paula Gómez Duran, Alexandros Karatzoglou, Jordi Vitrià +2
Modern recommender systems (RS) work by processing a number of signals that can be inferred from large sets of user-item interaction data. The main signal to analyze stems from the…
My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking
Luis A. Leiva, Ioannis Arapakis, Costas Iordanou
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. We de…
Impact of Response Latency on User Behaviour in Mobile Web Search
Ioannis Arapakis, Souneil Park, Martin Pielot
Traditionally, the efficiency and effectiveness of search systems have both been of great interest to the information retrieval community. However, an in-depth analysis of the inte…
Query Abandonment Prediction with Recurrent Neural Models of Mouse Cursor Movements
Lukas Brückner, Ioannis Arapakis, Luis A. Leiva
Most successful search queries do not result in a click if the user can satisfy their information needs directly on the SERP. Modeling query abandonment in the absence of click-thr…
Self-Supervised Reinforcement Learning for Recommender Systems
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
In session-based or sequential recommendation, it is important to consider a number of factors like long-term user engagement, multiple types of user-item interactions such as clic…
Learning Efficient Representations of Mouse Movements to Predict User Attention
Ioannis Arapakis, Luis A. Leiva
Tracking mouse cursor movements can be used to predict user attention on heterogeneous page layouts like SERPs. So far, previous work has relied heavily on handcrafted features, wh…
Graph Highway Networks
Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis +1
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency. However, they suffer from the notorious over-smoothing…
A Price-Per-Attention Auction Scheme Using Mouse Cursor Information
Ioannis Arapakis, Antonio Penta, Hideo Joho +1
Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just c…
A Simple Convolutional Generative Network for Next Item Recommendation
Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis +2
Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation. An ordered collection of past items the user has interac…
Typical Phone Use Habits: Intense Use Does Not Predict Negative Well-Being
Kleomenis Katevas, Ioannis Arapakis, Martin Pielot
Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicate…