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Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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發(fā)表于 2025-3-21 19:49:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Web and Big Data
副標(biāo)題8th International Jo
編輯Wenjie Zhang,Anthony Tung,Hongjie Guo
視頻videohttp://file.papertrans.cn/1022/1021661/1021661.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and
描述.The five-volume set LNCS 14961, 14962, 14963, 14964 and 14965 constitutes the refereed proceedings of the 8th International Joint Conference on Web and Big Data, APWeb-WAIM 2024, held in Jinhua, China, during August 30–September 1, 2024...The 171 full papers presented in these proceedings were carefully reviewed and selected from 558 submissions...The papers are organized in the following topical sections:.Part I:?Natural language processing,?Generative AI and LLM,?Computer Vision and?Recommender System...Part II:?Recommender System,?Knowledge Graph and Spatial and Temporal Data...Part III:?Spatial and Temporal Data,?Graph Neural Network,?Graph Mining and?Database System and Query Optimization...Part IV:?Database System and Query Optimization,?Federated and Privacy-Preserving Learning,?Network, Blockchain and Edge computing,?Anomaly Detection and Security..Part V:?Anomaly Detection and Security,?Information Retrieval,?Machine Learning,?Demonstration Paper and?Industry Paper..
出版日期Conference proceedings 2024
關(guān)鍵詞Machine Learning; Data mining; Graph data, RDF, social networks; Natural language processing; Knowledge
版次1
doihttps://doi.org/10.1007/978-981-97-7235-3
isbn_softcover978-981-97-7234-6
isbn_ebook978-981-97-7235-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Hierarchical Review-Based Recommendation with?Contrastive Collaborational gated sentiment-aware model for rating prediction in this paper. Specifically, to automatically suppress the influence of noisy reviews, we propose a hierarchical gating network to select informative textual signals at different levels of granularity. Specifically, a local gating module is propos
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Adaptive Augmentation and?Neighbor Contrastive Learning for?Multi-Behavior Recommendation Edge weights are neglected when constructing augmented views based on the interaction graph. It leads to the omission of crucial nodes or edges during the augmentation process. (2) During contrastive learning, positive pairs are adopted by using topology structure. However, we argue that semantic s
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Contrastive Generator Generative Adversarial Networks for?Sequential Recommendationon their historical interactions. However, these methods have some drawbacks as they require an effective generative model and training procedure to produce satisfactory results. These disadvantages include: 1) generative models lacking better generators for generative item sequences that user may b
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Distribution-Aware Diversification for?Personalized Re-ranking in?Recommendationking stage, as the final stage of the recommendation system, has a direct impact on the recommendation results. Many works dedicate to improving the diversity of recommendation systems in the re-ranking stage, but most of them optimize diversity based on traditional pairwise distance between element
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