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297 results on '"Shi Yuxiang"'

1. Transmutation and Action Path of Teachers’ Role in the Educational Metaverse——A Perspective on the Theory of Social Role

Author

Shi, Yuxiang, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Chen, Youbin, editor, Khan, Intakhab Alam, editor, Shen, Chaoqun, editor, and Mishra, Deepanjali, editor

Published
2024
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2. Task Aligned Meta-learning based Augmented Graph for Cold-Start Recommendation

Author

Shi, Yuxiang, Ding, Yue, Chen, Bo, Huang, Yuyang, Wang, Yule, Tang, Ruiming, and Wang, Dong

Subjects
Computer Science - Information Retrieval
Abstract

The cold-start problem is a long-standing challenge in recommender systems due to the lack of user-item interactions, which significantly hurts the recommendation effect over new users and items. Recently, meta-learning based methods attempt to learn globally shared prior knowledge across all users, which can be rapidly adapted to new users and items with very few interactions. Though with significant performance improvement, the globally shared parameter may lead to local optimum. Besides, they are oblivious to the inherent information and feature interactions existing in the new users and items, which are critical in cold-start scenarios. In this paper, we propose a Task aligned Meta-learning based Augmented Graph (TMAG) to address cold-start recommendation. Specifically, a fine-grained task aligned constructor is proposed to cluster similar users and divide tasks for meta-learning, enabling consistent optimization direction. Besides, an augmented graph neural network with two graph enhanced approaches is designed to alleviate data sparsity and capture the high-order user-item interactions. We validate our approach on three real-world datasets in various cold-start scenarios, showing the superiority of TMAG over state-of-the-art methods for cold-start recommendation.

Published
2022

9. Extracting Attentive Social Temporal Excitation for Sequential Recommendation

Author

Li, Yunzhe, Ding, Yue, Chen, Bo, Xin, Xin, Wang, Yule, Shi, Yuxiang, Tang, Ruiming, and Wang, Dong

Subjects
Computer Science - Social and Information Networks, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

In collaborative filtering, it is an important way to make full use of social information to improve the recommendation quality, which has been proved to be effective because user behavior will be affected by her friends. However, existing works leverage the social relationship to aggregate user features from friends' historical behavior sequences in a user-level indirect paradigm. A significant defect of the indirect paradigm is that it ignores the temporal relationships between behavior events across users. In this paper, we propose a novel time-aware sequential recommendation framework called Social Temporal Excitation Networks (STEN), which introduces temporal point processes to model the fine-grained impact of friends' behaviors on the user s dynamic interests in an event-level direct paradigm. Moreover, we propose to decompose the temporal effect in sequential recommendation into social mutual temporal effect and ego temporal effect. Specifically, we employ a social heterogeneous graph embedding layer to refine user representation via structural information. To enhance temporal information propagation, STEN directly extracts the fine-grained temporal mutual influence of friends' behaviors through the mutually exciting temporal network. Besides, the user s dynamic interests are captured through the self-exciting temporal network. Extensive experiments on three real-world datasets show that STEN outperforms state-of-the-art baseline methods. Moreover, STEN provides event-level recommendation explainability, which is also illustrated experimentally., Comment: Accepted by CIKM 2021

Published
2021
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40. A curtain purification system based on rabbit fur-based rotating triboelectric nanogenerator for efficient photocatalytic degradation of volatile organic compounds

Author

Yang, Dehong, primary, Liu, Zhaoqi, additional, Yang, Peng, additional, Huang, Ling, additional, Huang, Fengjiao, additional, Tao, Xinglin, additional, Shi, Yuxiang, additional, Lei, Rui, additional, Cao, Jiazhen, additional, Li, Hexing, additional, Chen, Xiangyu, additional, and Bian, Zhenfeng, additional

Published
2023
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