Search Results for author: Shoujin Wang

Found 18 papers, 3 papers with code

Word Sense Disambiguation with Knowledge-Enhanced and Local Self-Attention-based Extractive Sense Comprehension

no code implementations 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.

Word Sense Disambiguation

A Counterfactual Collaborative Session-based Recommender System

1 code implementation31 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.

Counterfactual Inference Recommendation Systems

Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspective

no code implementations27 Jan 2023 Hui He, Qi Zhang, Shoujin Wang, Kun Yi, Zhendong Niu, Longbing Cao

To address this significant gap, we formulate the MTS fairness modeling problem as learning informative representations attending to both advantaged and disadvantaged variables.

Fairness Multivariate Time Series Forecasting

A Systematical Evaluation for Next-Basket Recommendation Algorithms

no code implementations7 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.

Next-basket recommendation Recommendation Systems

Trustworthy Recommender Systems

no code implementations10 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.

Recommendation Systems

Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

no code implementations22 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.

Session-Based Recommendations

Aspect-driven User Preference and News Representation Learning for News Recommendation

no code implementations12 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.

News Recommendation Recommendation Systems +1

Next-item Recommendations in Short Sessions

no code implementations15 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.

Few-Shot Learning Recommendation Systems +1

Graph Learning based Recommender Systems: A Review

1 code implementation13 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).

Collaborative Filtering Graph Learning +1

Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations

no code implementations15 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.

Ensemble Learning Recommendation Systems +1

Double-Wing Mixture of Experts for Streaming Recommendations

no code implementations14 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.

Ensemble Learning Recommendation Systems

Jointly Modeling Intra- and Inter-transaction Dependencies with Hierarchical Attentive Transaction Embeddings for Next-item Recommendation

no code implementations30 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.

Recommendation Systems

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation

no code implementations8 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.

Tumor Segmentation

Sequential Recommender Systems: Challenges, Progress and Prospects

no code implementations28 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.

Collaborative Filtering Recommendation Systems

A Survey on Session-based Recommender Systems

1 code implementation13 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.

Collaborative Filtering Decision Making +1

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