Search Results for author: Alexandros Karatzoglou

Found 25 papers, 10 papers with code

Latent User Intent Modeling for Sequential Recommenders

no code implementations17 Nov 2022 Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen

We demonstrate the effectiveness of the latent user intent modeling via offline analyses as well as live experiments on a large-scale industrial recommendation platform.

Recommendation Systems

Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective

no code implementations15 Jun 2022 Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren

As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.

Recommendation Systems reinforcement-learning +1

Supervised Advantage Actor-Critic for Recommender Systems

no code implementations5 Nov 2021 Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose

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.

Q-Learning Reinforcement Learning (RL) +1

Enhancing Top-N Item Recommendations by Peer Collaboration

no code implementations31 Oct 2021 Yang Sun, Fajie Yuan, Min Yang, Alexandros Karatzoglou, Shen Li, Xiaoyan Zhao

In this paper, we plan to exploit such redundancy phenomena to improve the performance of RS.

Recommendation Systems

Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning

no code implementations28 Oct 2021 Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Kleomenis Katevas

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

On Interpretation and Measurement of Soft Attributes for Recommendation

no code implementations19 May 2021 Krisztian Balog, Filip Radlinski, Alexandros Karatzoglou

We address how to robustly interpret natural language refinements (or critiques) in recommender systems.

Recommendation Systems

Graph Convolutional Embeddings for Recommender Systems

no code implementations5 Mar 2021 Paula Gómez Duran, Alexandros Karatzoglou, Jordi Vitrià, Xin Xin, Ioannis Arapakis

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.

Collaborative Filtering Recommendation Systems

Untangle: Critiquing Disentangled Recommendations

no code implementations1 Jan 2021 Preksha Nema, Alexandros Karatzoglou, Filip Radlinski

Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.

Collaborative Filtering

One Person, One Model, One World: Learning Continual User Representation without Forgetting

2 code implementations29 Sep 2020 Fajie Yuan, Guoxiao Zhang, Alexandros Karatzoglou, Joemon Jose, Beibei Kong, Yudong Li

In this paper, we delve on research to continually learn user representations task by task, whereby new tasks are learned while using partial parameters from old ones.

Recommendation Systems

Graph Highway Networks

1 code implementation9 Apr 2020 Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose

Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.

Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation

1 code implementation13 Jan 2020 Fajie Yuan, Xiangnan He, Alexandros Karatzoglou, Liguang Zhang

To overcome this issue, we develop a parameter efficient transfer learning architecture, termed as PeterRec, which can be configured on-the-fly to various downstream tasks.

Recommendation Systems Transfer Learning

A Simple Convolutional Generative Network for Next Item Recommendation

3 code implementations15 Aug 2018 Fajie Yuan, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose, Xiangnan He

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of session-based next item recommendation.

Recommendation Systems

RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising

1 code implementation2 Aug 2018 David Rohde, Stephen Bonner, Travis Dunlop, Flavian vasile, Alexandros Karatzoglou

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 friend recommendation in social networks.

Product Recommendation Recommendation Systems +2

Towards a universal neural network encoder for time series

no code implementations10 May 2018 Joan Serrà, Santiago Pascual, Alexandros Karatzoglou

We evaluate the performance of the proposed approach on a well-known time series classification benchmark, considering full adaptation, partial adaptation, and no adaptation of the encoder to the new data type.

Time Series Time Series Analysis +1

Overcoming catastrophic forgetting with hard attention to the task

2 code implementations ICML 2018 Joan Serrà, Dídac Surís, Marius Miron, Alexandros Karatzoglou

In this paper, we propose a task-based hard attention mechanism that preserves previous tasks' information without affecting the current task's learning.

Continual Learning Hard Attention

Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks

no code implementations13 Jun 2017 Joan Serrà, Alexandros Karatzoglou

Due to the structure of the data coming from recommendation domains (i. e., one-hot-encoded vectors of item preferences), these algorithms tend to have large input and output dimensionalities that dominate their overall size.

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

2 code implementations13 Jun 2017 Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi, Paolo Cremonesi

Session-based recommendations are highly relevant in many modern on-line services (e. g. e-commerce, video streaming) and recommendation settings.

Session-Based Recommendations

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

11 code implementations ICLR 2018 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.

Collaborative Filtering Data Augmentation +2

Hot or not? Forecasting cellular network hot spots using sector performance indicators

no code implementations18 Apr 2017 Joan Serrà, Ilias Leontiadis, Alexandros Karatzoglou, Konstantina Papagiannaki

Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14% better forecasts for regular hot spots and 153% better forecasts for non-regular hot spots.

On Context-Dependent Clustering of Bandits

no code implementations ICML 2017 Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Evans Etrue, Giovanni Zappella

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.


Graph Clustering Bandits for Recommendation

no code implementations2 May 2016 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 users.

Clustering Graph Clustering +2

A genetic algorithm to discover flexible motifs with support

1 code implementation16 Nov 2015 Joan Serrà, Aleksandar Matic, Josep Luis Arcos, Alexandros Karatzoglou

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.

Time Series Time Series Analysis

Collaborative Filtering Bandits

no code implementations11 Feb 2015 Shuai Li, Alexandros Karatzoglou, Claudio Gentile

Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users.

Clustering Collaborative Filtering +1

Cannot find the paper you are looking for? You can Submit a new open access paper.