Search Results for author: Xin Xin

Found 17 papers, 7 papers with code

GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation

1 code implementation20 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).

Supervised Advantage Actor-Critic for Recommender Systems

no code implementations5 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.

Q-Learning Sequential Recommendation

Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning

no code implementations28 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.

reinforcement-learning Sequential Recommendation +1

Extracting Attentive Social Temporal Excitation for Sequential Recommendation

no code implementations28 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.

Collaborative Filtering Graph Embedding +2

ICMT: Item Cluster-Wise Multi-Objective Training for Long-Tail Recommendation

no code implementations27 Sep 2021 Yule Wang, Xin Xin, Yue Ding, Yunzhe Li, Yuxiang Shi, Dong Wang

Normal training methods from such biased data tend to repetitively generate recommendations from the head items, which further exacerbates the data bias and affects the exploration of potentially interesting items from niche (tail) items.

Recommendation Systems Representation Learning

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues

1 code implementation1 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.

Contrastive Learning Dialogue Generation +1

N-ary Constituent Tree Parsing with Recursive Semi-Markov Model

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.

Binarization Constituency Parsing

Learning Robust Recommenders through Cross-Model Agreement

no code implementations20 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.

Denoising Recommendation Systems

AutoDebias: Learning to Debias for Recommendation

1 code implementation10 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.

Imputation Meta-Learning +1

Graph Convolutional Embeddings for Recommender Systems

no code implementations5 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.

Collaborative Filtering Recommendation Systems

Should Graph Convolution Trust Neighbors? A Simple Causal Inference Method

1 code implementation22 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.

Causal Inference Graph Attention +2

Graph Highway Networks

1 code implementation9 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.

Generalized Embedding Machines for Recommender Systems

no code implementations16 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).

Recommendation Systems

Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation

2 code implementations29 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.

Collaborative Filtering Recommendation Systems

Pulsar Candidate Identification with Artificial Intelligence Techniques

no code implementations27 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.

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