21 citations · 76 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…