no code implementations • 21 Apr 2025 • Junchen Fu, Xuri Ge, Xin Xin, HaiTao Yu, Yue Feng, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Multimodal representation learning has garnered significant attention in the AI community, largely due to the success of large pre-trained multimodal foundation models like LLaMA, GPT, Mistral, and CLIP.
no code implementations • 23 Jan 2025 • Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Recent advancements in Recommender Systems (RS) have incorporated Reinforcement Learning (RL), framing the recommendation as a Markov Decision Process (MDP).
no code implementations • 5 Nov 2024 • Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Kaiwen Zheng, Yongxin Ni, Joemon M. Jose
To overcome this, we developed IISAN-Versa, a versatile plug-and-play architecture compatible with both symmetrical and asymmetrical MFMs.
1 code implementation • 2 Oct 2024 • Angela Lopez-Cardona, Carlos Segura, Alexandros Karatzoglou, Sergi Abadal, Ioannis Arapakis
Advancements in Natural Language Processing (NLP), have led to the emergence of Large Language Models (LLMs) such as GPT, Llama, Claude, and Gemini, which excel across a range of tasks but require extensive fine-tuning to align their outputs with human expectations.
no code implementations • 2 Aug 2024 • Liam Hebert, Krishna Sayana, Ambarish Jash, Alexandros Karatzoglou, Sukhdeep Sodhi, Sumanth Doddapaneni, Yanli Cai, Dima Kuzmin
Understanding the nuances of a user's extensive interaction history is key to building accurate and personalized natural language systems that can adapt to evolving user preferences.
2 code implementations • 2 Apr 2024 • Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
no code implementations • 25 Mar 2024 • Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
The LE is learned from a subset of user-item interaction data, thus reducing the need for large training data, and can synthesise user feedback for offline data by: (i) acting as a state model that produces high quality states that enrich the user representation, and (ii) functioning as a reward model to accurately capture nuanced user preferences on actions.
no code implementations • 17 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.
no code implementations • 15 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.
no code implementations • 5 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.
no code implementations • 31 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.
no code implementations • 28 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.
no code implementations • 19 May 2021 • Krisztian Balog, Filip Radlinski, Alexandros Karatzoglou
We address how to robustly interpret natural language refinements (or critiques) in recommender systems.
no code implementations • 5 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.
no code implementations • 1 Jan 2021 • Preksha Nema, Alexandros Karatzoglou, Filip Radlinski
Untangle gives control on critiquing recommendations based on users preferences, without sacrificing on recommendation accuracy.
2 code implementations • 29 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.
no code implementations • 10 Jun 2020 • Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
A major component of RL approaches is to train the agent through interactions with the environment.
1 code implementation • 9 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.
1 code implementation • 13 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.
3 code implementations • 15 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.
1 code implementation • 2 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.
no code implementations • 10 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.
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.
Ranked #2 on
Continual Learning
on 20Newsgroup (10 tasks)
no code implementations • 13 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.
2 code implementations • 13 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.
12 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.
no code implementations • 18 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.
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.
no code implementations • 2 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.
25 code implementations • 21 Nov 2015 • Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
Ranked #2 on
Recommendation Systems
on MovieLens 20M
(nDCG@10 (full corpus) metric)
1 code implementation • 16 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.
no code implementations • 11 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.