標(biāo)題: Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings [打印本頁(yè)] 作者: Waterproof 時(shí)間: 2025-3-21 17:43
書目名稱Database Systems for Advanced Applications影響因子(影響力)
書目名稱Database Systems for Advanced Applications影響因子(影響力)學(xué)科排名
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度
書目名稱Database Systems for Advanced Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Database Systems for Advanced Applications被引頻次
書目名稱Database Systems for Advanced Applications被引頻次學(xué)科排名
書目名稱Database Systems for Advanced Applications年度引用
書目名稱Database Systems for Advanced Applications年度引用學(xué)科排名
書目名稱Database Systems for Advanced Applications讀者反饋
書目名稱Database Systems for Advanced Applications讀者反饋學(xué)科排名
作者: BUMP 時(shí)間: 2025-3-21 21:44 作者: 健壯 時(shí)間: 2025-3-22 04:22 作者: 進(jìn)取心 時(shí)間: 2025-3-22 07:58
Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Rto alleviate the data sparsity issue. While recent studies demonstrate the effectiveness of cross-domain recommendation systems, there exist two unsolved challenges: (1) existing methods focus on transferring knowledge to generate shared factors implicitly, which fail to distill domain-shared featur作者: 驚呼 時(shí)間: 2025-3-22 11:23
Enhancing Graph Convolution Network for Novel Recommendationems yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail ite作者: Jejune 時(shí)間: 2025-3-22 14:41
Knowledge-Enhanced Multi-task Learning for?Course RecommendationAdaptive learning systems mainly generate course recommendations based on learner’s knowledge level acquired by KT. However, for KT tasks, learners’ forgetting has not been well modeled. In addition, learner’s individual differences also influence the accuracy of knowledge level prediction. While fo作者: Jejune 時(shí)間: 2025-3-22 18:28
Learning Social Influence from?Network Structure for?Recommender Systemsmethods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We 作者: Vaginismus 時(shí)間: 2025-3-22 23:43
PMAR: Multi-aspect Recommendation Based on Psychological Gapts have their descriptions of the items. The inconsistency between the descriptions and the actual attributes of items will bring users psychological gap caused by the Expectation Effect. Compared with the recommendation without merchant’s description, users may feel more unsatisfied with the items 作者: 不開心 時(shí)間: 2025-3-23 02:04
Meta-path Enhanced Lightweight Graph Neural Network for?Social Recommendationnd user-item interaction graphs, many previous social recommender systems model the information diffusion process in both graphs to obtain high-order information. We argue that this approach does not explicitly encode high-order connectivity, resulting in potential collaborative signals between user作者: Dungeon 時(shí)間: 2025-3-23 07:46 作者: Hay-Fever 時(shí)間: 2025-3-23 13:36 作者: 高歌 時(shí)間: 2025-3-23 15:00
Joint Locality Preservation and?Adaptive Combination for?Graph Collaborative Filtering-items as a bipartite graph, has become the upstart in recommender systems. Nevertheless, existing GCNs based recommendation model only compromisingly exploits the shallow relationship (generally less than 4 layers) to represent the user and item with different number of interactions, which limits t作者: immunity 時(shí)間: 2025-3-23 18:02
Gated Hypergraph Neural Network for?Scene-Aware Recommendationisting methods only explore one or some certain components of the entire interactions. In fact, the entire interaction process is much richer and more complex, including but not limited to “who purchases what items in which merchant?under what interaction environments”. Furthermore, many interaction作者: 南極 時(shí)間: 2025-3-24 01:56
Hyperbolic Personalized Tag Recommendationg to users’ tagging preferences. The main challenge of PTR is to learn representations of involved entities (i.e., users, items, and tags) from interaction data without loss of structural properties in original data. To this end, various PTR models have been developed to conduct representation learn作者: Daily-Value 時(shí)間: 2025-3-24 05:42 作者: DIS 時(shí)間: 2025-3-24 10:01 作者: addict 時(shí)間: 2025-3-24 10:57 作者: 黃油沒有 時(shí)間: 2025-3-24 17:28 作者: 巧辦法 時(shí)間: 2025-3-24 22:29
Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki作者: COMMA 時(shí)間: 2025-3-25 01:06 作者: Lipoprotein 時(shí)間: 2025-3-25 05:59 作者: DAFT 時(shí)間: 2025-3-25 07:32
Inter- and Intra-Domain Relation-Aware Heterogeneous Graph Convolutional Networks for Cross-Domain Rrogeneous graphs from ratings and reviews to preserve inter- and intra-domain relations. Then, a relation-aware graph convolutional network is designed to simultaneously distill domain-shared and domain-specific features, by exploring the multi-hop heterogeneous connections across different graphs. 作者: BACLE 時(shí)間: 2025-3-25 13:19
Enhancing Graph Convolution Network for Novel Recommendationtrue negative popular samples. Extensive experimental results on three datasets demonstrate that our method outperforms both graph based and novelty oriented baselines by a large margin in terms of the overall F-measure.作者: liposuction 時(shí)間: 2025-3-25 19:52 作者: gratify 時(shí)間: 2025-3-25 21:57 作者: Exterior 時(shí)間: 2025-3-26 01:39
PMAR: Multi-aspect Recommendation Based on Psychological Gaps overall and personalized psychological gaps. Specifically, we first design a gap logit unit for learning the user’s overall psychological gap towards items derived from textual review and merchant’s description. We then integrate a user-item co-attention mechanism to calculate the user’s personali作者: anus928 時(shí)間: 2025-3-26 04:43 作者: COW 時(shí)間: 2025-3-26 11:43 作者: 蕨類 時(shí)間: 2025-3-26 14:04
Multi-view Multi-behavior Contrastive Learning in?Recommendation differences of different behaviors. In experiments, we conduct extensive evaluations and ablation tests to verify the effectiveness of MMCLR and various CL tasks on two real-world datasets, achieving SOTA performance over existing baselines. Our code will be available on ..作者: 陶醉 時(shí)間: 2025-3-26 18:01 作者: 性上癮 時(shí)間: 2025-3-26 23:47 作者: 拔出 時(shí)間: 2025-3-27 01:41
Hyperbolic Personalized Tag Recommendationovel PTR model that operates on hyperbolic space, namely HPTR. HPTR learns the representations of entities by modeling their interactive relationships in hyperbolic space and utilizes hyperbolic distance to measure semantic relevance between entities. Specially, we adopt tangent space optimization t作者: 背書 時(shí)間: 2025-3-27 05:30
Diffusion-Based Graph Contrastive Learning for?Recommendation with?Implicit Feedbacks. A symmetric contrastive learning objective is used to contrast local node representations of the diffusion graph with those of the user-item interaction graph for learning better user and item representations. Extensive experiments on real datasets demonstrate that GDCL consistently outperforms s作者: Consensus 時(shí)間: 2025-3-27 11:48
Multi-behavior Recommendation with?Two-Level Graph Attentional Networksn, we learn the dynamic feature of target users and target items by modeling the dependency relation between them. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art recommendation methods.作者: DEFT 時(shí)間: 2025-3-27 15:27 作者: 我不重要 時(shí)間: 2025-3-27 19:42 作者: albuminuria 時(shí)間: 2025-3-27 22:07
César Fernández-de-las-Pe?as,Kimberly Bensen and fuse users’ personalized preferences on different modalities with a multi-modal probabilistic graph. Then, to filter out irrelevant and redundant information in multi-modal data, we extend the information bottleneck theory from single-modal to multi-modal scenario and design a multi-modal infor作者: 支柱 時(shí)間: 2025-3-28 04:55
Occlusal Diagnosis and Treatment of TMDrogeneous graphs from ratings and reviews to preserve inter- and intra-domain relations. Then, a relation-aware graph convolutional network is designed to simultaneously distill domain-shared and domain-specific features, by exploring the multi-hop heterogeneous connections across different graphs. 作者: Emg827 時(shí)間: 2025-3-28 10:13 作者: cognizant 時(shí)間: 2025-3-28 10:59
https://doi.org/10.1007/978-3-319-99909-8l, we not only design a personalized controller to enhance the deep knowledge tracing model for modeling learner’s forgetting behavior, but also use personality to model the individual differences based on the theory of cognitive psychology. In CRT, we adaptively combine learner’s knowledge level ob作者: 聯(lián)想 時(shí)間: 2025-3-28 17:02 作者: chronology 時(shí)間: 2025-3-28 18:51
Pediatric Temporomandibular Joint Surgerys overall and personalized psychological gaps. Specifically, we first design a gap logit unit for learning the user’s overall psychological gap towards items derived from textual review and merchant’s description. We then integrate a user-item co-attention mechanism to calculate the user’s personali作者: GREEN 時(shí)間: 2025-3-29 00:24 作者: 手銬 時(shí)間: 2025-3-29 05:49 作者: Hdl348 時(shí)間: 2025-3-29 08:40 作者: legacy 時(shí)間: 2025-3-29 13:59 作者: Emg827 時(shí)間: 2025-3-29 19:04
Contemporary Marketing Strategye hypergraph to model the entire interactions and scene prior knowledge. Then we propose a novel scene-aware gate mechanism-based hypergraph neural network to enrich their representations. Finally, we design a separable score function to predict the matching scores among user, scene, merchant?and in作者: 割公牛膨脹 時(shí)間: 2025-3-29 20:20 作者: Factual 時(shí)間: 2025-3-30 00:25 作者: Cardiac-Output 時(shí)間: 2025-3-30 04:59
Philosophical Evaluations of Systems Theoryn, we learn the dynamic feature of target users and target items by modeling the dependency relation between them. The results show that our model achieves great improvement for recommendation accuracy compared with other state-of-the-art recommendation methods.作者: 西瓜 時(shí)間: 2025-3-30 09:12
https://doi.org/10.1007/978-94-009-6268-2hanism is used to aggregate the social influence of users on the target user and the correlative items’ influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GN作者: tic-douloureux 時(shí)間: 2025-3-30 12:22
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/263437.jpg作者: 刻苦讀書 時(shí)間: 2025-3-30 19:33 作者: GENUS 時(shí)間: 2025-3-31 00:15
https://doi.org/10.1007/978-981-19-9542-2mmendation list is crucial for user-oriented applications. Many knowledge-based approaches combine graph neural networks with exploring node structural similarity, while paying little attention to semantically distinguishing potential user interests and item attributes. Therefore, personalized node 作者: aneurysm 時(shí)間: 2025-3-31 03:27 作者: inchoate 時(shí)間: 2025-3-31 06:34
César Fernández-de-las-Pe?as,Kimberly Bensentworks (GNN) are widely applied to SBR due to their superiority on learning better item and session embeddings. However, existing GNN-based SBR models mainly leverage direct neighbors, lacking efficient utilization of multi-hop neighbors information. To address this issue, we propose a multi-head gr作者: Myosin 時(shí)間: 2025-3-31 10:56 作者: 感染 時(shí)間: 2025-3-31 14:43
Occlusal Diagnosis and Treatment of TMDems yet neglect tail ones, which are actually the focus of novel recommendation since they can provide more surprises for users and more profits for enterprises. Furthermore, current novelty oriented methods treat all users equally without considering their personal preference on popular or tail ite作者: Ondines-curse 時(shí)間: 2025-3-31 21:18 作者: Neuropeptides 時(shí)間: 2025-3-31 23:42
Larry Wolford,Jacinto Fernandez Sanromanmethods focus on incorporating the semantic collaborative information of social friends. In this paper, we argue that the semantic strength of their friends is also influenced by the subnetwork structure of friendship groups, which had not been well addressed in social recommendation literature. We 作者: 金哥占卜者 時(shí)間: 2025-4-1 02:48
Pediatric Temporomandibular Joint Surgeryts have their descriptions of the items. The inconsistency between the descriptions and the actual attributes of items will bring users psychological gap caused by the Expectation Effect. Compared with the recommendation without merchant’s description, users may feel more unsatisfied with the items 作者: perjury 時(shí)間: 2025-4-1 09:16
Baber Khatib,Allen Cheng,Eric Dierksnd user-item interaction graphs, many previous social recommender systems model the information diffusion process in both graphs to obtain high-order information. We argue that this approach does not explicitly encode high-order connectivity, resulting in potential collaborative signals between user作者: Figate 時(shí)間: 2025-4-1 12:05
https://doi.org/10.1007/978-3-319-99915-9wadays, such as e-commerce and short video recommendation. There is a common scenario that user specifies a target category of items as a global filter, however previous SBR settings mainly consider the item sequence and overlook the rich target category information. Therefore, we define a new task 作者: acquisition 時(shí)間: 2025-4-1 18:10
https://doi.org/10.1007/978-3-319-99915-9 should: (1) model the coarse-grained commonalities between different behaviors of a user, (2) consider both individual sequence view and global graph view in multi-behavior modeling, and (3) capture the fine-grained differences between multiple behaviors of a user. In this work, we propose a novel 作者: Lime石灰 時(shí)間: 2025-4-1 20:34
Understanding Market Environment-items as a bipartite graph, has become the upstart in recommender systems. Nevertheless, existing GCNs based recommendation model only compromisingly exploits the shallow relationship (generally less than 4 layers) to represent the user and item with different number of interactions, which limits t作者: 衰老 時(shí)間: 2025-4-1 23:19
Contemporary Marketing Strategyisting methods only explore one or some certain components of the entire interactions. In fact, the entire interaction process is much richer and more complex, including but not limited to “who purchases what items in which merchant?under what interaction environments”. Furthermore, many interaction
