2024 Intent contrastive learning for sequential

Intent contrastive learning for sequential

Author: saep

August undefined, 2024

WebApr 14, 2024 · Bu et al. introduce hypergraph learning to music recommender systems, which is the earliest attempt. Li et al. propose a novel architecture named hyperbolic hypergraph representation learning method for sequential recommendation (\(H^{2}\) SeqRec) with the pre-training phase. Unlike their works, our proposed model exploits inter … WebFeb 27, 2024 · Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views. To address… View on ACM dl.acm.org Save to Library Create Alert Cite Figures and Tables from this …

Equivariant Contrastive Learning for Sequential Recommendation

WebApr 14, 2024 · In this paper, we propose a Knowledge graph enhanced Recommendation with Context awareness and Contrastive learning (KRec-C2) to overcome the issue. Specifically, we design an category-level ... WebApr 14, 2024 · We propose a global contrastive data-enhancement framework for the sequential recommendation, termed GUESR, where the graph contrastive learning is … middle ga orthopedics

Intent Contrastive Learning for Sequential Recommendation

WebAug 14, 2024 · In this paper, we investigate the application of contrastive Self- Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues. Contrastive SSL constructs augmentations from unlabelled instances, where agreements among positive pairs are maximized. WebContrastive learning with Transformer-based sequence encoder has gainedpredominance for sequential recommendation. It maximizes the agreements betweenpaired sequence augmentations that share similar semantics. However, existingcontrastive learning approaches in sequential recommendation mainly center uponleft-to-right unidirectional … WebOct 28, 2024 · Chen Y, Liu Z, Li J et al (2024) Intent Contrastive Learning for Sequential Recommendation. In: Proceedings of the ACM web conference 2024, pp 2172–2182. Wang Z, Zhang J, Xu H et al (2024) Counterfactual data-augmented sequential recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and … middle ga ortho perry

A multi-intent based multi-policy relay contrastive learning for ...

Intent Contrastive Learning for Sequential Recommendation

WebApr 25, 2024 · Intent contrastive learning (ICL) [8] performs -means clustering on user representations; the centroids of the clusters are used as intent representations. Li et al. … WebApr 6, 2024 · A novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec) is proposed, which not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences. middle ga orthopaedichttp://export.arxiv.org/abs/2202.02519 middle ga republican women

"WebString”2, whose intent is opposite to the intent of the query. BIKER fails to retrieve the correct answer for the above query because of its bag-of-words-based representation, which cannot capture the semantic-related sequential information. To properly represent the semantic-related sequential information of the text descrip- " - Intent contrastive learning for sequential

Intent contrastive learning for sequential

WebThe intent representation learning module and the contrastive SSL module are mutually reinforced to train a more expressive sequence encoder. We tackle the challenge of intent … WebExisting contrastive learning methods mainly rely on data level augmentation for user-item interaction sequences through item cropping, masking, or reordering and can hardly provide semantically consistent augmentation samples. In DuoRec, a model-level augmentation is proposed based on Dropout to enable better semantic preserving.

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WebAwesome Contrastive Learning & Data Augmentation RS Paper & Code This repository collects the latest research progress of Contrastive Learning (CL) and Data Augmentation (DA) in Recommender Systems. Comments and contributions are welcome. CF = Collaborative Filtering, SSL = Self-Supervised Learning Survey/Tutorial Total Papers: 4 WebContrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's …

WebOct 27, 2024 · CL4SRec not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals … WebApr 3, 2024 · 鉴于上述局限性和挑战，作者采用一种简单而有效的LightGCL增强方法，重新审视图对比学习范式。. 在该模型中，图的增强由奇异值分解（SVD）指导，不仅提取用户-项目交互的有用信息，而且还将全局协作上下文注入到对比学习的表示对齐中。. 使用这样一种鲁 …

WebAug 31, 2024 · The recent contrastive learning (CL) has shown potential in mitigating the issue of data sparsity. Many item representations are destined to be poorly learned due to data sparsity. It is better... WebWe propose to leverage the learnt intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. The train- ing is alternated between intent representation learning and the SR model optimization steps within the generalized expectation- maximization (EM) framework.

WebFeb 5, 2024 · We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding …

WebFeb 5, 2024 · We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding … middle ga orthopedics perry gaWebNov 10, 2024 · Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals. Existing solutions apply general … middle ga orthopedics warner robinsWebMay 12, 2024 · Contrastive Learning for Sequential Recommendation Abstract: Sequential recommendation methods play a crucial role in modern recommender systems because of their ability to capture a user's dynamic interest from her/his historical inter-actions. middle ga ortho physical therapyWebApr 14, 2024 · Through contrastive learning, we can alleviate the redundancy and noise issues and enhance the robustness of the model. We use a contrastive learning … middle ga state university academic calendarWebFeb 5, 2024 · We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding … new space invaders led cabinetWebApr 14, 2024 · 【天大】Multi-Intentions Oriented Contrastive Learning for Sequential Recommendation【面向多意图的序列推荐对比学习】 ... 【港大，微软】Efficiently Leveraging Multi-level User Intent for Session-based Recommendation via Atten-Mixer Network【通过Atten Mixer网络有效利用多级用户意图进行基于会话的 ... middle ga ortho warner robins gaWebJan 1, 2024 · First, we integrate temporal density information into sequential recommendation when capturing user preferences. In detail, through our proposed Temporal KDE Module, we map timestamps into temporal density vectors aiming at improving the recommendation performance. Second, we introduce contrastive learning into TDSRec to … newspace linkedin