Dynamic Embeddings for Interaction Prediction

Kefato, Zekarias T.; Girdzijauskas, Šarūnas; Sheikh, Nasrullah; Montresor, Alberto

doi:10.1145/3442381.3450020

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…With regard to representing sequential interaction networks, a significant amount of work has been proposed to learn the user and item embeddings from their historical interaction records in Euclidean space [4,6,12,14,27,33]. Recurrent models such as Time-LSTM [59], Time-Aware LSTM [3] and RRN [53] capture dynamics of users and items by endowing them with a long short-term memory.…”

Section: Datasetmentioning

confidence: 99%

“…• Random walk based models: CAW [51], CTDNE [33] are two temporal network models adopting causal and anonymous random walks. • Sequence network embedding models: in this category, JODIE [28], HILI [10] and DeePRed [27] are three state-ofthe-art methods in generating embeddings from sequential networks employing recursive networks. • Hyperbolic models: we compare SINCERE with HGCF [42] which is a hyperbolic method on collaborative filtering.…”

Section: Experiments 41 Datasets and Compared Algorithmsmentioning

confidence: 99%

“…CTDNE [33] and CAW [51] adopt random walk to temporal graph modeling. Co-evolution models consider the influence between users and items and achieve great success [10,12,27,28]. For systematic and comprehensive reviews, readers may refer to [1,26].…”

Section: Related Workmentioning

confidence: 99%

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SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

Zhang

et al. 2023

Proceedings of the ACM Web Conference 2023

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Sequential interaction networks (SIN) have been commonly adopted in many applications such as recommendation systems, search engines and social networks to describe the mutual influence between users and items/products. Efforts on representing SIN are mainly focused on capturing the dynamics of networks in Euclidean space, and recently plenty of work has extended to hyperbolic geometry for implicit hierarchical learning. Previous approaches which learn the embedding trajectories of users and items achieve promising results. However, there are still a range of fundamental issues remaining open. For example, is it appropriate to place user and item nodes in one identical space regardless of their inherent discrepancy? Instead of residing in a single fixed curvature space, how will the representation spaces evolve when new interaction occurs?To explore these implication for sequential interaction networks, we propose SINCERE, a novel method representing Sequential Interaction Networks on Co-Evolving RiEmannian manifolds. SIN-CERE not only takes the user and item embedding trajectories in respective spaces into account, but also emphasizes on the space evolvement that how curvature changes over time. Specifically, we introduce a fresh cross-geometry aggregation which allows us to propagate information across different Riemannian manifolds without breaking conformal invariance, and a curvature estimator which is delicately designed to predict global curvatures effectively according to current local Ricci curvatures. Extensive experiments on several real-world datasets demonstrate the promising performance of SINCERE over the state-of-the-art sequential interaction prediction methods.

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Section: Datasetmentioning

confidence: 99%

Section: Experiments 41 Datasets and Compared Algorithmsmentioning

confidence: 99%

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SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

Zhang

et al. 2023

Proceedings of the ACM Web Conference 2023

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“…To investigate this subject, we conduct an in-depth study by referencing previous research [14], [17]. We develop a template to process movie reviews from a deliberately selected public dataset using LLMs to generate user and item representations.…”

Section: Introductionmentioning

confidence: 99%

Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced Representations

Wang

2024

IEEE Access

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Recommender systems utilizing explicit feedback have witnessed significant advancements and widespread applications over the past years. However, generating recommendations in few-shot scenarios remains a persistent challenge. Recently, large language models (LLMs) have emerged as a promising solution for addressing natural language processing (NLP) tasks, thereby offering novel insights into tackling the few-shot scenarios encountered by explicit feedback-based recommender systems. To bridge recommender systems and LLMs, we devise a prompting template that generates user and item representations based on explicit feedback. Subsequently, we integrate these LLM-processed representations into various recommendation models to evaluate their significance across diverse recommendation tasks.Our ablation experiments and case study analysis collectively demonstrate the effectiveness of LLMs in processing explicit feedback, highlighting that LLMs equipped with generative and logical reasoning capabilities can effectively serve as a component of recommender systems to enhance their performance in few-shot scenarios. Furthermore, the broad adaptability of LLMs augments the generalization potential of recommender models, despite certain inherent constraints. We anticipate that our study can inspire researchers to delve deeper into the multifaceted dimensions of LLMs' involvement in recommender systems and contribute to the advancement of the explicit feedback-based recommender systems field.INDEX TERMS Large language models, recommender systems, ChatGPT, representations.

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LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks

Giaretta

Lekssays

Carminati

et al. 2021

Computer Security – ESORICS 2021

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IoT devices have been growing exponentially in the last few years. This growth makes them an attractive target for attackers due to their low computational power and limited security features. Attackers use IoT botnets as an instrument to perform DDoS attacks which caused major disruptions of Internet services in the last decade. While many works have tackled the task of detecting botnet attacks, only a few have considered early-stage detection of these botnets during their propagation phase. While previous approaches analyze each network packet individually to predict its maliciousness, we propose a novel deep learning model called LiMNet (Lightweight Memory Network), which uses an internal memory component to capture the behaviour of each IoT device over time. This memory incorporates both packet features and behaviour of the peer devices. With this information, LiMNet achieves almost maximum AUROC classification scores, between 98.8% and 99.7%, with a 14% improvement over state of the art. LiMNet is also lightweight, performing inference almost 8 times faster than previous approaches.

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Dynamic Embeddings for Interaction Prediction

Cited by 6 publications

References 32 publications

SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds

Empowering Few-Shot Recommender Systems With Large Language Models-Enhanced Representations

LiMNet: Early-Stage Detection of IoT Botnets with Lightweight Memory Networks

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