963 citations · 2.6k across the 21 of their papers we have counts for
11 papers · 1 filter
Latent User Intent Modeling for Sequential Recommenders
Bo Chang, Alexandros Karatzoglou, Yuyan Wang +3
Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with base…
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective
Xin Xin, Tiago Pimentel, Alexandros Karatzoglou +3
Modern recommender systems aim to improve user experience. As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become…
Enhancing Top-N Item Recommendations by Peer Collaboration
Yang Sun, Fajie Yuan, Min Yang +3
Deep neural networks (DNN) have achieved great success in the recommender systems (RS) domain. However, to achieve remarkable performance, DNN-based recommender models often requir…
On Interpretation and Measurement of Soft Attributes for Recommendation
Krisztian Balog, Filip Radlinski, Alexandros Karatzoglou
We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use…
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…
One Person, One Model, One World: Learning Continual User Representation without Forgetting
Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou +3
Learning user representations is a vital technique toward effective user modeling and personalized recommender systems. Existing approaches often derive an individual set of model…
Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
Fajie Yuan, Xiangnan He, Alexandros Karatzoglou +1
Inductive transfer learning has had a big impact on computer vision and NLP domains but has not been used in the area of recommender systems. Even though there has been a large bod…
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising
David Rohde, Stephen Bonner, Travis Dunlop +2
Recommender Systems are becoming ubiquitous in many settings and take many forms, from product recommendation in e-commerce stores, to query suggestions in search engines, to frien…
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…
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
Linas Baltrunas, Karen Church, Alexandros Karatzoglou +1
This paper describes a real world deployment of a context-aware mobile app recommender system (RS) called Frappe. Utilizing a hybrid-approach, we conducted a large-scale app market…
GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas +2
Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. If accurate top-N recommendation lists…