標(biāo)題: Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and [打印本頁] 作者: 稀少 時(shí)間: 2025-3-21 19:49
書目名稱Web and Big Data影響因子(影響力)
書目名稱Web and Big Data影響因子(影響力)學(xué)科排名
書目名稱Web and Big Data網(wǎng)絡(luò)公開度
書目名稱Web and Big Data網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Web and Big Data被引頻次
書目名稱Web and Big Data被引頻次學(xué)科排名
書目名稱Web and Big Data年度引用
書目名稱Web and Big Data年度引用學(xué)科排名
書目名稱Web and Big Data讀者反饋
書目名稱Web and Big Data讀者反饋學(xué)科排名
作者: CLIFF 時(shí)間: 2025-3-21 22:48
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作者: 相信 時(shí)間: 2025-3-22 03:40 作者: 香料 時(shí)間: 2025-3-22 07:56
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作者: FLORA 時(shí)間: 2025-3-22 11:28 作者: Commemorate 時(shí)間: 2025-3-22 14:16 作者: 無能性 時(shí)間: 2025-3-22 17:36
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作者: coalition 時(shí)間: 2025-3-22 21:34 作者: GLARE 時(shí)間: 2025-3-23 02:40
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作者: Directed 時(shí)間: 2025-3-23 06:26 作者: 聽覺 時(shí)間: 2025-3-23 13:12 作者: cornucopia 時(shí)間: 2025-3-23 15:38 作者: cocoon 時(shí)間: 2025-3-23 19:04
Logic Preference Fusion Reasoning on?Recommendationtract user preferences from interaction records, they frequently neglect the user’s logical requirements, which are embedded in the logical relations between items and entities. Existing methods that account for user’s logical requirements employ neural networks to mimic logical operators, failing t作者: 叢林 時(shí)間: 2025-3-24 00:11
Logic Preference Fusion Reasoning on?Recommendationtract user preferences from interaction records, they frequently neglect the user’s logical requirements, which are embedded in the logical relations between items and entities. Existing methods that account for user’s logical requirements employ neural networks to mimic logical operators, failing t作者: Ointment 時(shí)間: 2025-3-24 06:26
MHGNN: Hybrid Graph Neural Network with?Mixers for?Multi-interest Session-Aware Recommendationevements of existing methods, they still have drawbacks in some aspects. Firstly, most existing methods only consider transition relationships between items within the current user’s sessions, while neglecting the valuable item transition patterns from other users and the useful preferences from sim作者: 一起 時(shí)間: 2025-3-24 07:39
MHGNN: Hybrid Graph Neural Network with?Mixers for?Multi-interest Session-Aware Recommendationevements of existing methods, they still have drawbacks in some aspects. Firstly, most existing methods only consider transition relationships between items within the current user’s sessions, while neglecting the valuable item transition patterns from other users and the useful preferences from sim作者: 苦澀 時(shí)間: 2025-3-24 12:51 作者: Charlatan 時(shí)間: 2025-3-24 15:23 作者: 要控制 時(shí)間: 2025-3-24 20:39 作者: 安心地散步 時(shí)間: 2025-3-25 00:04
Noise-Resistant Graph Neural Networks for?Session-Based Recommendationclick of a user based on a short anonymous interaction sequence. Previous works have focused on users’ long-term and short-term preferences, ignoring the noise problem in session sequences. However, session data is inevitably noisy, as it may contain incorrect clicks that are inconsistent with the u作者: 開始發(fā)作 時(shí)間: 2025-3-25 05:26
S2DNMF: A Self-supervised Deep Nonnegative Matrix Factorization Recommendation Model Incorporating Dem, this paper proposes a recommendation model based on deep nonnegative matrix factorization (Self-supervised Deep Nonnegative Matrix Factorization, .), which inherits the advantages of the self-supervised model, combines deep attribute fusion features of network structure, integrates network topol作者: 脆弱么 時(shí)間: 2025-3-25 10:18
S2DNMF: A Self-supervised Deep Nonnegative Matrix Factorization Recommendation Model Incorporating Dem, this paper proposes a recommendation model based on deep nonnegative matrix factorization (Self-supervised Deep Nonnegative Matrix Factorization, .), which inherits the advantages of the self-supervised model, combines deep attribute fusion features of network structure, integrates network topol作者: notice 時(shí)間: 2025-3-25 13:53
Self-filtering Residual Attention Network Based on?Multipair Information Fusion for?Session-Based Res (i.e., interaction) to predict the next interact item in the session. However, under the auspices of user anonymity and short activity durations, data sparsity is a significant problem for these models. Moreover, given that human users rarely follow a scripted session, many noisy interact items ca作者: 減至最低 時(shí)間: 2025-3-25 19:26 作者: 斷言 時(shí)間: 2025-3-25 23:04
TransRec: Learning Transferable Recommendation from?Mixture-of-Modality Feedbackr, current recommendation methods often rely on categorical identity features that cannot be shared between different platforms, making fine-tuning models for new scenarios challenging. Displayed content on these platforms often contain multimedia information, leading to a mixture-of-modality (MoM) 作者: spinal-stenosis 時(shí)間: 2025-3-26 02:23
TransRec: Learning Transferable Recommendation from?Mixture-of-Modality Feedbackr, current recommendation methods often rely on categorical identity features that cannot be shared between different platforms, making fine-tuning models for new scenarios challenging. Displayed content on these platforms often contain multimedia information, leading to a mixture-of-modality (MoM) 作者: travail 時(shí)間: 2025-3-26 05:16
VM-Rec: A Variational Mapping Approach for?Cold-Start User Recommendationiciency in auxiliary content information for users. Furthermore, most methods often require simultaneous updates to extensive parameters of recommender models, resulting in high training costs, especially in large-scale industrial scenarios. We observe that the model can generate expressive embeddin作者: NEEDY 時(shí)間: 2025-3-26 09:34 作者: 使殘廢 時(shí)間: 2025-3-26 14:43
Matching Tabular Data to?Knowledge Graph Based on?Multi-level Scoring Filters for?Table Entity Disamee tasks: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). It is a non-trivial task due to missing, incomplete, or ambiguous metadata, which makes entity disambiguation more difficult. Previous approaches mostly are based on two representative paradi作者: 生命層 時(shí)間: 2025-3-26 19:43
Matching Tabular Data to?Knowledge Graph Based on?Multi-level Scoring Filters for?Table Entity Disamee tasks: Column Type Annotation (CTA), Cell Entity Annotation (CEA), and Columns Property Annotation (CPA). It is a non-trivial task due to missing, incomplete, or ambiguous metadata, which makes entity disambiguation more difficult. Previous approaches mostly are based on two representative paradi作者: 辯論的終結(jié) 時(shí)間: 2025-3-26 22:14
Complex Knowledge Base Question Answering via?Structure and?Content Dual-Driven Methodnowledge-based question answering (KBQA) has attracted much attention in the application field of knowledge base. Semantic-parsing-based KBQA methods perform question-answering tasks by executing constructed query graphs on the graph database. However, current semantic-parsing-based KBQA methods sti作者: 面包屑 時(shí)間: 2025-3-27 05:06
Complex Knowledge Base Question Answering via?Structure and?Content Dual-Driven Methodnowledge-based question answering (KBQA) has attracted much attention in the application field of knowledge base. Semantic-parsing-based KBQA methods perform question-answering tasks by executing constructed query graphs on the graph database. However, current semantic-parsing-based KBQA methods sti作者: 人類學(xué)家 時(shí)間: 2025-3-27 09:10
EvoREG: Evolutional Modeling with?Relation-Entity Dual-Guidance for?Temporal Knowledge Graph Reasoniresearch hotspot. Typically, TKG reasoning approaches mainly concentrate on modeling interactions among entities, while the abundant semantic interactions among relations are almost neglected. Besides, TKG reasoning approaches mostly utilize a single graph convolutional network (GCN) to learn repres作者: 蒸發(fā) 時(shí)間: 2025-3-27 12:33
EvoREG: Evolutional Modeling with?Relation-Entity Dual-Guidance for?Temporal Knowledge Graph Reasoniresearch hotspot. Typically, TKG reasoning approaches mainly concentrate on modeling interactions among entities, while the abundant semantic interactions among relations are almost neglected. Besides, TKG reasoning approaches mostly utilize a single graph convolutional network (GCN) to learn repres作者: 季雨 時(shí)間: 2025-3-27 15:54
Conference proceedings 2024nd 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,?Ge作者: 一再遛 時(shí)間: 2025-3-27 17:46 作者: periodontitis 時(shí)間: 2025-3-27 23:22
Qi Wang,Anbiao Wu,Ye Yuan,Yishu Wang,Guangqing Zhong,Xuefeng Gao,Chenghu Yang作者: 招募 時(shí)間: 2025-3-28 03:40 作者: 上流社會(huì) 時(shí)間: 2025-3-28 07:27 作者: 溫室 時(shí)間: 2025-3-28 12:11 作者: 形上升才刺激 時(shí)間: 2025-3-28 17:40
Linan Zheng,Jiale Chen,Pengsheng Liu,Guangfa Zhang,Jinyun Fang作者: aquatic 時(shí)間: 2025-3-28 22:29 作者: Banquet 時(shí)間: 2025-3-29 00:08
Zihong Wang,Yingxia Shao,Jiyuan He,Jinbao Liu (semantics) – and expressivity of the languages to represent and manipulate the data, information and knowledge – syntax, semantics. There are related issues of security and trust, of heterogeneity and distribution and of scheduling and performance. The key architectural components are metadata, ag作者: 外科醫(yī)生 時(shí)間: 2025-3-29 06:35
Yingtao Peng,Tangpeng Dan,Zhendong Zhao,Aishan Maoliniyazi,Xiaofeng Mengciplines, as well as interrelationships among them and opportunities for cross-fertilization. The chosen topics are: Pervasive Computing and Communications; Nanoelectronics and nanotechnology; Security, dependability and trust; Bio-ICT synergies; Intelligent and Cognitive Systems; Software Intensive作者: 確保 時(shí)間: 2025-3-29 10:14
Yingtao Peng,Tangpeng Dan,Zhendong Zhao,Aishan Maoliniyazi,Xiaofeng Mengcompressibility measures (often within .(.) bits), and support . in doubly-logarithmic (or better) time, and experimental evaluations of practical variants thereof..A . is a data structure that supports ‘./.’ on a bit-string, and is fundamental to succinct and compressed data structures. We describe作者: Rebate 時(shí)間: 2025-3-29 14:20 作者: 諷刺滑稽戲劇 時(shí)間: 2025-3-29 15:54 作者: 娘娘腔 時(shí)間: 2025-3-29 22:03 作者: 終點(diǎn) 時(shí)間: 2025-3-30 01:09 作者: minaret 時(shí)間: 2025-3-30 05:24 作者: 侵略者 時(shí)間: 2025-3-30 11:27
Zhuolun Dong,Yan Yang,Yingli Zhongaltelementen gebildete Maschen vorhanden sein, deren gesamter Gleichstromwiderstand gleich Null ist (Bilder 2.0.1 g, h, i), als Abhilfe kann ein niederohmiger Hilfswiderstand in die Masche geschaltet werden, der die Schaltungseigenschaften nicht beeinflu?t.作者: prodrome 時(shí)間: 2025-3-30 15:30
Ronghua Zhang,Wei Song,Limengzi Yuan,Changzheng Liur Gruppe derjenigen, die im Unterricht abschalten, bzw. zur Gruppe derjenigen, die im Unterricht nicht abschalten, besteht. Damit stellt sich die folgende Frage: “Kann man aus den vorhandenen Daten eine Zuordnungsregel entwickeln, die es erlaubt, weitere Schüler und Schülerinnen allein auf der Kennt作者: 物質(zhì) 時(shí)間: 2025-3-30 19:47 作者: MAIM 時(shí)間: 2025-3-30 21:20 作者: 條街道往前推 時(shí)間: 2025-3-31 04:52 作者: AUGER 時(shí)間: 2025-3-31 06:06
r a long-standing question in this area of investigation by establishing the density of the Solovay degrees. We also provide a new characterization of the random c.e. reals in terms of splittings in the Solovay degrees. Specifically, we show that the Solovay degrees of computably enumerable reals ar作者: countenance 時(shí)間: 2025-3-31 12:29 作者: Stricture 時(shí)間: 2025-3-31 14:30 作者: 侵略主義 時(shí)間: 2025-3-31 19:56 作者: nugatory 時(shí)間: 2025-3-31 23:02
Jinhao Zhang,Lizong Zhang,Jinchuan Zhang,Yichen Xin,Xu Zhengnt their result can be sharpened. Using the layered label cover problem recently introduced by Dinur . [STOC 2003], we devise a new “outer verifier” that allows us to construct an “inner verifier” that uses the query bits more efficiently than earlier verifiers. This enables us to construct a PCP fo作者: 講個(gè)故事逗他 時(shí)間: 2025-4-1 03:14 作者: Cryptic 時(shí)間: 2025-4-1 09:40 作者: murmur 時(shí)間: 2025-4-1 13:58
Hierarchical Review-Based Recommendation with?Contrastive Collaboration the target ratings for developing self-supervision signals. Finally, extensive experiments on public datasets and comparison studies with state-of-the-art baselines have demonstrated the effectiveness of the proposed model, additional investigations also provide a deep insight into the rationale un作者: 壯觀的游行 時(shí)間: 2025-4-1 17:10 作者: 蛤肉 時(shí)間: 2025-4-1 19:17 作者: phytochemicals 時(shí)間: 2025-4-1 23:00 作者: Maximizer 時(shí)間: 2025-4-2 05:39 作者: 赦免 時(shí)間: 2025-4-2 08:43
Automated Modeling of?Influence Diversity with?Graph Convolutional Network for?Social Recommendations. The tailored aggregation mechanism automatically estimates the importance of each graph perspective for each user, reflecting the degree to which each user is influenced by social connections. This mechanism can model influence diversity in a manner that is highly interpretable, less prone to ran作者: 難解 時(shí)間: 2025-4-2 13:02
Contrastive Generator Generative Adversarial Networks for?Sequential Recommendationuence and fake sequence. Additionally, we enhance the Discriminator by combining the Wasserstein loss with a ranking loss, creating a joint loss function.This combination better distinguishes between generated sequences and ground truth data, guiding the Generator towards producing item sequences th作者: Negotiate 時(shí)間: 2025-4-2 17:38 作者: Fracture 時(shí)間: 2025-4-2 22:42
Distribution-Aware Diversification for?Personalized Re-ranking in?Recommendationthe number of categories in the list while making the distribution of categories more balanced and personalized than traditional re-ranking methods. We conduct experiments on two public datasets and one private dataset, the results demonstrate that our proposed method effectively improves the recomm作者: PACK 時(shí)間: 2025-4-3 00:13
Distribution-Aware Diversification for?Personalized Re-ranking in?Recommendationthe number of categories in the list while making the distribution of categories more balanced and personalized than traditional re-ranking methods. We conduct experiments on two public datasets and one private dataset, the results demonstrate that our proposed method effectively improves the recomm作者: anus928 時(shí)間: 2025-4-3 06:08
KMIC: A Knowledge-Aware Recommendation with?Multivariate Intentions Contrastive Learningely models the multivariate intent as attentive learning of interaction relations, which encourages the personalization awareness of different intents for better model capability. Besides, we design a multi-intents contrastive learning module (MCLM), which mines comprehensive intention information a作者: LEERY 時(shí)間: 2025-4-3 09:51
KMIC: A Knowledge-Aware Recommendation with?Multivariate Intentions Contrastive Learningely models the multivariate intent as attentive learning of interaction relations, which encourages the personalization awareness of different intents for better model capability. Besides, we design a multi-intents contrastive learning module (MCLM), which mines comprehensive intention information a作者: locus-ceruleus 時(shí)間: 2025-4-3 13:34
