activity
20202024
most citedLeveraging Large Language Models for Sequential Recommendation

127 citations · 165 across the 7 of their papers we have counts for

collaborators

9 papers

cs.DC2024

CheckMate: Evaluating Checkpointing Protocols for Streaming Dataflows

George Siachamis, Kyriakos Psarakis, Marios Fragkoulis +3

Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors t…

cs.IR2024★ 13 cited

Improving Sequential Recommendations with LLMs

Artun Boz, Wouter Zorgdrager, Zoe Kotti +5

The sequential recommendation problem has attracted considerable research attention in the past few years, leading to the rise of numerous recommendation models. In this work, we e…

cs.DC2023★ 3 cited

Styx: Transactional Stateful Functions on Streaming Dataflows

Kyriakos Psarakis, George Christodoulou, George Siachamis +2

Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Function…

cs.IR2023★ 127 cited

Leveraging Large Language Models for Sequential Recommendation

Jesse Harte, Wouter Zorgdrager, Panos Louridas +3

Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches.…

cs.DB2022★ 1 cited

SiMa: Effective and Efficient Matching Across Data Silos Using Graph Neural Networks

Christos Koutras, Rihan Hai, Kyriakos Psarakis +2

How can we leverage existing column relationships within silos, to predict similar ones across silos? Can we do this efficiently and effectively? Existing matching approaches do no…

cs.DC2021

Stateful Entities: Object-oriented Cloud Applications as Distributed Dataflows

Kyriakos Psarakis, Wouter Zorgdrager, Marios Fragkoulis +2

Although the cloud has reached a state of robustness, the burden of using its resources falls on the shoulders of programmers who struggle to keep up with ever-growing cloud infras…

cs.DC2021★ 1 cited

Hazelcast Jet: Low-latency Stream Processing at the 99.99th Percentile

Can Gencer, Marko Topolnik, Viliam Ďurina +11

Jet is an open-source, high-performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th perc…

cs.DB2020

Valentine: Evaluating Matching Techniques for Dataset Discovery

Christos Koutras, George Siachamis, Andra Ionescu +6

Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of…

cs.DC2020★ 20 cited

A Survey on the Evolution of Stream Processing Systems

Marios Fragkoulis, Paris Carbone, Vasiliki Kalavri +1

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and n…