no code implementations • 16 Apr 2024 • Ziqi Zhao, Zhaochun Ren, Liu Yang, Fajie Yuan, Pengjie Ren, Zhumin Chen, Jun Ma, Xin Xin
Then we propose four strategies to use World Transformers to generate high-rewarded trajectory simulation by perturbing the offline data.
2 code implementations • 2 Apr 2024 • Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Jie Wang, Joemon M. Jose
This is also a notable improvement over the Adapter and LoRA, which require 37-39 GB GPU memory and 350-380 seconds per epoch for training.
no code implementations • 27 Mar 2024 • Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
1 code implementation • 25 Mar 2024 • Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.
1 code implementation • 22 Dec 2023 • Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren
On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.
1 code implementation • 12 Dec 2023 • Jiyuan Yang, Yue Ding, Yidan Wang, Pengjie Ren, Zhumin Chen, Fei Cai, Jun Ma, Rui Zhang, Zhaochun Ren, Xin Xin
Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.
1 code implementation • 4 Nov 2023 • Shiguang Wu, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Maarten de Rijke, Zhaochun Ren
CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.
no code implementations • 2 Oct 2023 • Zhiyu Peng, Bayu Jayawardhana, Xin Xin
The presence of faulty or underactuated manipulators can disrupt the end-effector formation keeping of a team of manipulators.
no code implementations • 14 Sep 2023 • Zhiyu Peng, Bayu Jayawardhana, Xin Xin
This paper addresses the problem of end-effector formation control for a mixed group of two-link manipulators moving in a horizontal plane that comprises of fully-actuated manipulators and underactuated manipulators with only the second joint being actuated (referred to as the passive-active (PA) manipulators).
1 code implementation • 27 Aug 2023 • Shen Gao, Zhengliang Shi, Minghang Zhu, Bowen Fang, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs.
no code implementations • 27 Aug 2023 • Yu Wang, Xin Xin, Zaiqiao Meng, Joemon Jose, Fuli Feng
We employ the proposed DeCA on both the binary label scenario and the multiple label scenario.
no code implementations • 19 Jul 2023 • Qingyao Ai, Ting Bai, Zhao Cao, Yi Chang, Jiawei Chen, Zhumin Chen, Zhiyong Cheng, Shoubin Dong, Zhicheng Dou, Fuli Feng, Shen Gao, Jiafeng Guo, Xiangnan He, Yanyan Lan, Chenliang Li, Yiqun Liu, Ziyu Lyu, Weizhi Ma, Jun Ma, Zhaochun Ren, Pengjie Ren, Zhiqiang Wang, Mingwen Wang, Ji-Rong Wen, Le Wu, Xin Xin, Jun Xu, Dawei Yin, Peng Zhang, Fan Zhang, Weinan Zhang, Min Zhang, Xiaofei Zhu
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
1 code implementation • 24 May 2023 • Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He
Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i. e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential.
1 code implementation • 18 May 2023 • Zhaochun Ren, Na Huang, Yidan Wang, Pengjie Ren, Jun Ma, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M Jose, Xin Xin
For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.
1 code implementation • 9 May 2023 • Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon Jose, Maarten de Rijke, Zhaochun Ren
Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.
1 code implementation • 4 Mar 2023 • Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
1 code implementation • 22 Dec 2022 • XiaoYu Zhang, Xin Xin, Dongdong Li, Wenxuan Liu, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren
We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.
1 code implementation • 16 Oct 2022 • Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, Zhaochun Ren
Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.
no code implementations • 15 Jun 2022 • Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren
As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.
1 code implementation • 20 May 2022 • Jiajia Chen, Xin Xin, Xianfeng Liang, Xiangnan He, Jun Liu
However, existing graph-based methods fails to consider the bias offsets of users (items).
no code implementations • 5 Nov 2021 • Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
However, the direct use of RL algorithms in the RS setting is impractical due to challenges like off-policy training, huge action spaces and lack of sufficient reward signals.
no code implementations • 28 Oct 2021 • Dusan Stamenkovic, Alexandros Karatzoglou, Ioannis Arapakis, Xin Xin, Kleomenis Katevas
The proposed SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
Multi-Objective Reinforcement Learning reinforcement-learning +2
no code implementations • 28 Sep 2021 • Yunzhe Li, Yue Ding, Bo Chen, Xin Xin, Yule Wang, Yuxiang Shi, Ruiming Tang, Dong Wang
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.
no code implementations • 27 Sep 2021 • Yule Wang, Xin Xin, Yue Ding, Yunzhe Li, Dong Wang
In detail, we define our item cluster-wise optimization target as the recommender model should balance all item clusters that differ in popularity, thus we set the model learning on each item cluster as a unique optimization objective.
1 code implementation • 1 Sep 2021 • Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen
(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.
1 code implementation • ACL 2021 • Xin Xin, Jinlong Li, Zeqi Tan
In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children.
Ranked #3 on Constituency Parsing on CTB5
no code implementations • 20 May 2021 • Yu Wang, Xin Xin, Zaiqiao Meng, Xiangnan He, Joemon Jose, Fuli Feng
A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference.
1 code implementation • 10 May 2021 • Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang
This provides a valuable opportunity to develop a universal solution for debiasing, e. g., by learning the debiasing parameters from data.
no code implementations • 5 Mar 2021 • Paula Gómez Duran, Alexandros Karatzoglou, Jordi Vitrià, Xin Xin, Ioannis Arapakis
In this work, we generalize the use of GCNs for N-partite graphs by considering N multiple context dimensions and propose a simple way for their seamless integration in modern deep learning RS architectures.
1 code implementation • 22 Oct 2020 • Fuli Feng, Weiran Huang, Xiangnan He, Xin Xin, Qifan Wang, Tat-Seng Chua
To this end, we analyze the working mechanism of GCN with causal graph, estimating the causal effect of a node's local structure for the prediction.
no code implementations • 10 Jun 2020 • Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
A major component of RL approaches is to train the agent through interactions with the environment.
1 code implementation • 9 Apr 2020 • Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency.
no code implementations • 16 Feb 2020 • Enneng Yang, Xin Xin, Li Shen, Guibing Guo
In this work, we propose an alternative approach to model high-order interaction signals in the embedding level, namely Generalized Embedding Machine (GEM).
2 code implementations • 29 Apr 2019 • Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, Joemon Jose
In this work, we propose Relational Collaborative Filtering (RCF), a general framework to exploit multiple relations between items in recommender system.
no code implementations • ACL 2018 • Xin Xin, Fajie Yuan, Xiangnan He, Joemon M. Jose
Stochastic Gradient Descent (SGD) with negative sampling is the most prevalent approach to learn word representations.
no code implementations • 27 Nov 2017 • Ping Guo, Fuqing Duan, Pei Wang, Yao Yao, Qian Yin, Xin Xin
To address these problems, we proposed a framework which combines deep convolution generative adversarial network (DCGAN) with support vector machine (SVM) to deal with imbalance class problem and to improve pulsar identification accuracy.