Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

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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

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健壯

進(jìn)取心

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

驚呼

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

Jejune

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

Jejune

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

Vaginismus

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

不開心

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

Dungeon