1 code implementation • COLING 2022 • Guobiao Zhang, Wenpeng Lu, Xueping Peng, Shoujin Wang, Baoshuo Kan, Rui Yu
Word sense disambiguation (WSD), identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory, is one of the most classical and challenging tasks in natural language processing.
1 code implementation • 17 Mar 2024 • Peilin Zhou, You-Liang Huang, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Sunghun Kim
Intriguingly, the conclusion drawn from our study is that, certain data augmentation strategies can achieve similar or even superior performance compared with some CL-based methods, demonstrating the potential to significantly alleviate the data sparsity issue with fewer computational overhead.
no code implementations • 18 Feb 2024 • Liang Xiao, Qi Zhang, Chongyang Shi, Shoujin Wang, Usman Naseem, Liang Hu
These existing methods fail to handle the complex, subtle twists in news articles, such as syntax-semantics mismatches and prior biases, leading to lower performance and potential failure when modalities or social context are missing.
no code implementations • 13 Feb 2024 • Jin Li, Shoujin Wang, Qi Zhang, Longbing Cao, Fang Chen, Xiuzhen Zhang, Dietmar Jannach, Charu C. Aggarwal
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
no code implementations • 5 Dec 2023 • Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, Xueping Peng
This methodological rigor establishes a cohesive framework for the impartial evaluation of diverse NBR approaches.
2 code implementations • NeurIPS 2023 • Kun Yi, Qi Zhang, Wei Fan, Shoujin Wang, Pengyang Wang, Hui He, Defu Lian, Ning An, Longbing Cao, Zhendong Niu
FreTS mainly involves two stages, (i) Domain Conversion, that transforms time-domain signals into complex numbers of frequency domain; (ii) Frequency Learning, that performs our redesigned MLPs for the learning of real and imaginary part of frequency components.
1 code implementation • 23 Aug 2023 • Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
1 code implementation • 18 Aug 2023 • Peilin Zhou, Qichen Ye, Yueqi Xie, Jingqi Gao, Shoujin Wang, Jae Boum Kim, Chenyu You, Sunghun Kim
Our empirical analysis of some representative Transformer-based SR models reveals that it is not uncommon for large attention weights to be assigned to less relevant items, which can result in inaccurate recommendations.
no code implementations • 23 May 2023 • Jiachang Liu, Qi Zhang, Chongyang Shi, Usman Naseem, Shoujin Wang, Ivor Tsang
Abstractive related work generation has attracted increasing attention in generating coherent related work that better helps readers grasp the background in the current research.
no code implementations • 27 Apr 2023 • Qi Zhang, Yayi Yang, Chongyang Shi, An Lao, Liang Hu, Shoujin Wang, Usman Naseem
Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET.
1 code implementation • 15 Apr 2023 • Sibo Wei, Wenpeng Lu, Xueping Peng, Shoujin Wang, Yi-Fei Wang, Weiyu Zhang
Although existing works have attempted to utilize Seq2Seq, reinforcement learning, or contrastive learning to solve the problem, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance.
no code implementations • 4 Feb 2023 • Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
1 code implementation • 31 Jan 2023 • Wenzhuo Song, Shoujin Wang, Yan Wang, Kunpeng Liu, Xueyan Liu, Minghao Yin
Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem.
1 code implementation • 27 Jan 2023 • Hui He, Qi Zhang, Shoujin Wang, Kun Yi, Zhendong Niu, Longbing Cao
To bridge such significant gap, we formulate the fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.
1 code implementation • 10 Nov 2022 • Peilin Zhou, Jingqi Gao, Yueqi Xie, Qichen Ye, Yining Hua, Jae Boum Kim, Shoujin Wang, Sunghun Kim
Therefore, we propose Equivariant Contrastive Learning for Sequential Recommendation (ECL-SR), which endows SR models with great discriminative power, making the learned user behavior representations sensitive to invasive augmentations (e. g., item substitution) and insensitive to mild augmentations (e. g., featurelevel dropout masking).
no code implementations • 7 Sep 2022 • Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, Xueping Peng
Different studies often evaluate NBR approaches on different datasets, under different experimental settings, making it hard to fairly and effectively compare the performance of different NBR approaches.
no code implementations • 1 Sep 2022 • Xinyu Jiang, Qi Zhang, Chongyang Shi, Kaiying Jiang, Liang Hu, Shoujin Wang
Story ending generation aims at generating reasonable endings for a given story context.
no code implementations • 10 Aug 2022 • Shoujin Wang, Xiuzhen Zhang, Yan Wang, Huan Liu, Francesco Ricci
However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs.
no code implementations • 29 Jul 2022 • Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang, Quan Z. Sheng, Shoujin Wang, Xiaoshui Huang, Zhenmei Yu
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.
no code implementations • 22 May 2022 • Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal
In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.
no code implementations • 12 Oct 2021 • Rongyao Wang, Wenpeng Lu, Shoujin Wang, Xueping Peng, Hao Wu, Qian Zhang
News recommender systems are essential for helping users to efficiently and effectively find out those interesting news from a large amount of news.
no code implementations • 15 Jul 2021 • Wenzhuo Song, Shoujin Wang, Yan Wang, Shengsheng Wang
The obtained similar sessions are then utilized to complement and optimize the preference representation learned from the current short session by the local module for more accurate next-item recommendations in this short session.
1 code implementation • 13 May 2021 • Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
no code implementations • 15 Sep 2020 • Yan Zhao, Shoujin Wang, Yan Wang, Hongwei Liu
To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations.
no code implementations • 14 Sep 2020 • Yan Zhao, Shoujin Wang, Yan Wang, Hongwei Liu, Weizhe Zhang
In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences.
no code implementations • 30 May 2020 • Shoujin Wang, Longbing Cao, Liang Hu, Shlomo Berkovsky, Xiaoshui Huang, Lin Xiao, Wenpeng Lu
Most existing TBRSs recommend next item by only modeling the intra-transaction dependency within the current transaction while ignoring inter-transaction dependency with recent transactions that may also affect the next item.
no code implementations • 8 May 2020 • Shuchao Pang, Anan Du, Mehmet A. Orgun, Yan Wang, Quanzheng Sheng, Shoujin Wang, Xiaoshui Huang, Zhemei Yu
To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.
no code implementations • 22 Apr 2020 • Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet Orgun, Longbing Cao, Nan Wang, Francesco Ricci, Philip S. Yu
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
no code implementations • 28 Dec 2019 • Shoujin Wang, Liang Hu, Yan Wang, Longbing Cao, Quan Z. Sheng, Mehmet Orgun
The emerging topic of sequential recommender systems has attracted increasing attention in recent years. Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time.
1 code implementation • 13 Feb 2019 • Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.