Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

Zhang, Peiyan; Guo, Jiayan; Li, Chaozhuo; Xie, Yueqi; Kim, Jae Boum; Zhang, Yan; Xie, Xing; Wang, Haohan; Kim, Sunghun

doi:10.1145/3539597.3570445

Cited by 57 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, many other deep learning models were also introduced to seek better performance, such as Recurrent Neural Network (RNN) [24,45], Convolutional Neural Network (CNN) [16,40], Graph Neural Network (GNN) [2,8,41,42,57], and Multilayer Perceptron (MLP) [61]. Except for the aforementioned models, attention-based models have also been intensively studied and widely adopted in sequential recommendation tasks [19,37,55]. Besides, there are many interesting ongoing works focusing on other techniques like contrastive learning [4,27,48,63,64], reinforcement learning [51], multi-interest learning [49], large language model [25,26,62] and relation awareness [14].…”

Section: Related Work 51 Sequential Recommendationmentioning

confidence: 99%

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Zhou,

Huang,

Xie

et al. 2024

Proceedings of the ACM Web Conference 2024

Self Cite

View full text Add to dashboard Cite

Sequential recommender systems (SRS) are designed to predict users' future behaviors based on their historical interaction data. Recent research has increasingly utilized contrastive learning (CL) to leverage unsupervised signals to alleviate the data sparsity issue in SRS. In general, CL-based SRS first augments the raw sequential interaction data by using data augmentation strategies and employs a contrastive training scheme to enforce the representations of those sequences from the same raw interaction data to be similar. Despite the growing popularity of CL, data augmentation, as a basic component of CL, has not received sufficient attention. This raises the question: Is it possible to achieve superior recommendation results solely through data augmentation? To answer this question, we benchmark eight widely used data augmentation strategies, as well as state-of-the-art CL-based SRS methods, on four real-world datasets under both warm-and cold-start settings. Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead. We hope that our study can further inspire more fundamental studies on the key functional components of complex CL techniques. Our processed datasets and codes are available at https://github.com/AIM-SE/DA4Rec.

show abstract

Section: Related Work 51 Sequential Recommendationmentioning

confidence: 99%

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Zhou,

Huang,

Xie

et al. 2024

Proceedings of the ACM Web Conference 2024

Self Cite

View full text Add to dashboard Cite

show abstract

“…GRADE (Wang et al 2022) proposes a graph contrastive learning method to enhance the inherent community effect of networks via data augmentation. Degree-related bias in graph-based recommendation is also known as cold-start problem, which is usually alleviated by introducing side information and constructing informative heterogeneous graphs, such as the profile of users and items (Zheng et al 2021;Zhang et al 2023), knowledge graphs (Wang et al 2019a) and social networks . There is also a recent work (Hao et al 2021) attempting to pre-train GNN-based recommendation models with reconstruction-based pretext task.…”

Section: Related Workmentioning

confidence: 99%

Tail-STEAK: Improve Friend Recommendation for Tail Users via Self-Training Enhanced Knowledge Distillation

Ma,

Li,

Zhou

2024

AAAI

View full text Add to dashboard Cite

Graph neural networks (GNNs) are commonly employed in collaborative friend recommendation systems. Nevertheless, recent studies reveal a notable performance gap, particularly for users with limited connections, commonly known as tail users, in contrast to their counterparts with abundant connections (head users). Uniformly treating head and tail users poses two challenges for tail user preference learning: (C1) Label Sparsity, as tail users typically possess limited labels; and (C2) Neighborhood Sparsity, where tail users exhibit sparse observable friendships, leading to distinct preference distributions and performance degradation compared to head users. In response to these challenges, we introduce Tail-STEAK, an innovative framework that combines self-training with enhanced knowledge distillation for tail user representation learning. To address(C1), we present Tail-STEAK-base, a two-stage self-training framework. In the first stage, only head users and their accurate connections are utilized for training, while pseudo links are generated for tail users in the second stage. To tackle (C2), we propose two data augmentation-based self-knowledge distillation pretext tasks. These tasks are seamlessly integrated into different stages of Tail-STEAK-base, culminating in the comprehensive Tail-STEAK framework. Extensive experiments, conducted on state-of-the-art GNN-based friend recommendation models, substantiate the efficacy of Tail-STEAK in significantly improving tail user performance. Our code and data are publicly available at https://github.com/antman9914/Tail-STEAK.

show abstract

“…To learn the final news representation, a modality attentive graph pooling module is proposed to capture the multimodal content hierarchically. AKA-Fake first learns the cluster assignment matrix and processes graph coarsening hierarchically (Zhang et al 2023). Then the adaptive knowledge graph is aggregated with other modalities .…”

Section: Modality Attentive Hierarchical Poolingmentioning

confidence: 99%

Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection

Zhang,

Zhou

et al. 2024

AAAI

View full text Add to dashboard Cite

Nowadays, detecting multimodal fake news has emerged as a foremost concern since the widespread dissemination of fake news may incur adverse societal impact. Conventional methods generally focus on capturing the linguistic and visual semantics within the multimodal content, which fall short in effectively distinguishing the heightened level of meticulous fabrications. Recently, external knowledge is introduced to provide valuable background facts as complementary to facilitate news detection. Nevertheless, existing knowledge-enhanced endeavors directly incorporate all knowledge contexts through static entity embeddings, resulting in the potential noisy and content-irrelevant knowledge. Moreover, the integration of knowledge entities makes it intractable to model the sophisticated correlations between multimodal semantics and knowledge entities. In light of these limitations, we propose a novel Adaptive Knowledge-Aware Fake News Detection model, dubbed AKA-Fake. For each news, AKA-Fake learns a compact knowledge subgraph under a reinforcement learning paradigm, which consists of a subset of entities and contextual neighbors in the knowledge graph, restoring the most informative knowledge facts. A novel heterogeneous graph learning module is further proposed to capture the reliable cross-modality correlations via topology refinement and modality-attentive pooling. Our proposal is extensively evaluated over three popular datasets, and experimental results demonstrate the superiority of AKA-Fake.

show abstract

Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network

Cited by 57 publications

References 29 publications

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation

Tail-STEAK: Improve Friend Recommendation for Tail Users via Self-Training Enhanced Knowledge Distillation

Reinforced Adaptive Knowledge Learning for Multimodal Fake News Detection

Contact Info

Product

Resources

About