963 citations · 2.6k across the 21 of their papers we have counts for
25 papers
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…
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…
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…
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…
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…
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…
Towards a universal neural network encoder for time series
Joan Serrà, Santiago Pascual, Alexandros Karatzoglou
We study the use of a time series encoder to learn representations that are useful on data set types with which it has not been trained on. The encoder is formed of a convolutional…
Overcoming catastrophic forgetting with hard attention to the task
Joan Serrà, Dídac Surís, Marius Miron +1
Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artifici…
Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks
Joan Serrà, Alexandros Karatzoglou
Recommendation algorithms that incorporate techniques from deep learning are becoming increasingly popular. Due to the structure of the data coming from recommendation domains (i.e…
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi +1
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Network…
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Balázs Hidasi, Alexandros Karatzoglou
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressiv…
Hot or not? Forecasting cellular network hot spots using sector performance indicators
Joan Serrà, Ilias Leontiadis, Alexandros Karatzoglou +1
To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score,…
On Context-Dependent Clustering of Bandits
Claudio Gentile, Shuai Li, Purushottam Kar +3
We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborho…
Graph Clustering Bandits for Recommendation
Shuai Li, Claudio Gentile, Alexandros Karatzoglou
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple us…
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas +1
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on…
A genetic algorithm to discover flexible motifs with support
Joan Serrà, Aleksandar Matic, Josep Luis Arcos +1
Finding repeated patterns or motifs in a time series is an important unsupervised task that has still a number of open issues, starting by the definition of motif. In this paper, w…
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…