Search Results for author: Chenliang Li

Found 76 papers, 38 papers with code

Adaptive Feature Discrimination and Denoising for Asymmetric Text Matching

no code implementations COLING 2022 Yan Li, Chenliang Li, Junjun Guo

Asymmetric text matching has becoming increasingly indispensable for many downstream tasks (e. g., IR and NLP).

Denoising Text Matching

PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation

no code implementations EMNLP 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

mPLUG-PaperOwl: Scientific Diagram Analysis with the Multimodal Large Language Model

1 code implementation30 Nov 2023 Anwen Hu, Yaya Shi, Haiyang Xu, Jiabo Ye, Qinghao Ye, Ming Yan, Chenliang Li, Qi Qian, Ji Zhang, Fei Huang

In this work, towards a more versatile copilot for academic paper writing, we mainly focus on strengthening the multi-modal diagram analysis ability of Multimodal LLMs.

Language Modelling Large Language Model

Bi-Preference Learning Heterogeneous Hypergraph Networks for Session-based Recommendation

1 code implementation2 Nov 2023 Xiaokun Zhang, Bo Xu, Fenglong Ma, Chenliang Li, Yuan Lin, Hongfei Lin

Secondly, price preference and interest preference are interdependent and collectively determine user choice, necessitating that we jointly consider both price and interest preference for intent modeling.

Multi-Task Learning Session-Based Recommendations

One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems

no code implementations22 Oct 2023 Zuoli Tang, ZhaoXin Huan, Zihao Li, Xiaolu Zhang, Jun Hu, Chilin Fu, Jun Zhou, Chenliang Li

We expect that by mixing the user's behaviors across different domains, we can exploit the common knowledge encoded in the pre-trained language model to alleviate the problems of data sparsity and cold start problems.

Language Modelling Question Answering +2

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

1 code implementation2 Sep 2023 Chenliang Li, Hehong Chen, Ming Yan, Weizhou Shen, Haiyang Xu, Zhikai Wu, Zhicheng Zhang, Wenmeng Zhou, Yingda Chen, Chen Cheng, Hongzhu Shi, Ji Zhang, Fei Huang, Jingren Zhou

Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.

BUS:Efficient and Effective Vision-language Pre-training with Bottom-Up Patch Summarization

no code implementations17 Jul 2023 Chaoya Jiang, Haiyang Xu, Wei Ye, Qinghao Ye, Chenliang Li, Ming Yan, Bin Bi, Shikun Zhang, Fei Huang, Songfang Huang

Specifically, We incorporate a Text-Semantics-Aware Patch Selector (TSPS) into the ViT backbone to perform a coarse-grained visual token extraction and then attach a flexible Transformer-based Patch Abstraction Decoder (PAD) upon the backbone for top-level visual abstraction.

Text Summarization

mPLUG-DocOwl: Modularized Multimodal Large Language Model for Document Understanding

1 code implementation4 Jul 2023 Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Yuhao Dan, Chenlin Zhao, Guohai Xu, Chenliang Li, Junfeng Tian, Qian Qi, Ji Zhang, Fei Huang

Nevertheless, without in-domain training, these models tend to ignore fine-grained OCR features, such as sophisticated tables or large blocks of text, which are essential for OCR-free document understanding.

document understanding Language Modelling +2

Multi-Scenario Ranking with Adaptive Feature Learning

no code implementations29 Jun 2023 Yu Tian, Bofang Li, Si Chen, Xubin Li, Hongbo Deng, Jian Xu, Bo Zheng, Qian Wang, Chenliang Li

Recently, Multi-Scenario Learning (MSL) is widely used in recommendation and retrieval systems in the industry because it facilitates transfer learning from different scenarios, mitigating data sparsity and reducing maintenance cost.

Retrieval Transfer Learning

Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks

1 code implementation7 Jun 2023 Haiyang Xu, Qinghao Ye, Xuan Wu, Ming Yan, Yuan Miao, Jiabo Ye, Guohai Xu, Anwen Hu, Yaya Shi, Guangwei Xu, Chenliang Li, Qi Qian, Maofei Que, Ji Zhang, Xiao Zeng, Fei Huang

In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.

Cross-Modal Retrieval Language Modelling +3

Unconfounded Propensity Estimation for Unbiased Ranking

no code implementations17 May 2023 Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen, Chenliang Li, Dawei Yin, Brian D. Davison

The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.


Transforming Visual Scene Graphs to Image Captions

no code implementations3 May 2023 Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu, Qinghao Ye, Chenliang Li, Ming Yan, Fei Huang, Zhangzikang Li, Yu Zhang

In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs.

Descriptive Image Captioning

On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training

no code implementations19 Apr 2023 Hao Fei, Tat-Seng Chua, Chenliang Li, Donghong Ji, Meishan Zhang, Yafeng Ren

In this study, we propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.

Aspect-Based Sentiment Analysis (ABSA) Contrastive Learning +1

ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human

1 code implementation16 Apr 2023 Junfeng Tian, Hehong Chen, Guohai Xu, Ming Yan, Xing Gao, Jianhai Zhang, Chenliang Li, Jiayi Liu, Wenshen Xu, Haiyang Xu, Qi Qian, Wei Wang, Qinghao Ye, Jiejing Zhang, Ji Zhang, Fei Huang, Jingren Zhou

In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.

World Knowledge

DiffuRec: A Diffusion Model for Sequential Recommendation

1 code implementation3 Apr 2023 Zihao Li, Aixin Sun, Chenliang Li

Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors.

Sequential Recommendation

Understanding Expertise through Demonstrations: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

1 code implementation15 Feb 2023 Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.

Autonomous Driving Continuous Control +2

mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video

4 code implementations1 Feb 2023 Haiyang Xu, Qinghao Ye, Ming Yan, Yaya Shi, Jiabo Ye, Yuanhong Xu, Chenliang Li, Bin Bi, Qi Qian, Wei Wang, Guohai Xu, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou

In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.

Action Classification Image Classification +7

Learning Trajectory-Word Alignments for Video-Language Tasks

no code implementations ICCV 2023 Xu Yang, Zhangzikang Li, Haiyang Xu, Hanwang Zhang, Qinghao Ye, Chenliang Li, Ming Yan, Yu Zhang, Fei Huang, Songfang Huang

To amend this, we propose a novel TW-BERT to learn Trajectory-Word alignment by a newly designed trajectory-to-word (T2W) attention for solving video-language tasks.

Question Answering Retrieval +4

BUS: Efficient and Effective Vision-Language Pre-Training with Bottom-Up Patch Summarization.

no code implementations ICCV 2023 Chaoya Jiang, Haiyang Xu, Wei Ye, Qinghao Ye, Chenliang Li, Ming Yan, Bin Bi, Shikun Zhang, Fei Huang, Songfang Huang

In this paper, we propose a Bottom-Up Patch Summarization approach named BUS which is inspired by the Document Summarization Task in NLP to learn a concise visual summary of lengthy visual token sequences, guided by textual semantics.

Abstractive Text Summarization Document Summarization

Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees

no code implementations4 Oct 2022 Siliang Zeng, Chenliang Li, Alfredo Garcia, Mingyi Hong

To reduce the computational burden of a nested loop, novel methods such as SQIL [1] and IQ-Learn [2] emphasize policy estimation at the expense of reward estimation accuracy.

counterfactual Imitation Learning +2

Global inference with explicit syntactic and discourse structures for dialogue-level relation extraction

1 code implementation Conference 2022 Hao Fei, Jingye Li, Shengqiong Wu, Chenliang Li, Donghong Ji, Fei Li

In our global reasoning framework, D2G and ARG work collaboratively, iteratively performing lexical, syntactic and semantic information exchange and representation learning over the entire dialogue context.

Dialog Relation Extraction Representation Learning

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

1 code implementation12 Jul 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Yuxuan Liang, Yanwei Yu, Chenliang Li

Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework.

Sequential Recommendation

Bi-VLDoc: Bidirectional Vision-Language Modeling for Visually-Rich Document Understanding

no code implementations27 Jun 2022 Chuwei Luo, Guozhi Tang, Qi Zheng, Cong Yao, Lianwen Jin, Chenliang Li, Yang Xue, Luo Si

Multi-modal document pre-trained models have proven to be very effective in a variety of visually-rich document understanding (VrDU) tasks.

Document Classification document understanding +2

Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading

no code implementations13 Jun 2022 Zitao Song, Xuyang Jin, Chenliang Li

In recent years, many practitioners in quantitative finance have attempted to use Deep Reinforcement Learning (DRL) to build better quantitative trading (QT) strategies.

Time Series Time Series Analysis

Automatic Expert Selection for Multi-Scenario and Multi-Task Search

no code implementations28 May 2022 Xinyu Zou, Zhi Hu, Yiming Zhao, Xuchu Ding, Zhongyi Liu, Chenliang Li, Aixin Sun

At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input.

Multi-Task Learning

mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections

3 code implementations24 May 2022 Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si

Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.

Image Captioning Question Answering +5

When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation

1 code implementation3 May 2022 Yu Tian, Jianxin Chang, Yannan Niu, Yang song, Chenliang Li

Specifically, multi-interest methods such as ComiRec and MIMN, focus on extracting different interests for a user by performing historical item clustering, while graph convolution methods including TGSRec and SURGE elect to refine user preferences based on multi-level correlations between historical items.

Sequential Recommendation

Knowledge Graph Contrastive Learning for Recommendation

1 code implementation2 May 2022 Yuhao Yang, Chao Huang, Lianghao Xia, Chenliang Li

However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities.

Contrastive Learning General Knowledge +3

Do Loyal Users Enjoy Better Recommendations? Understanding Recommender Accuracy from a Time Perspective

1 code implementation12 Apr 2022 Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li

Our study offers a different perspective to understand recommender accuracy, and our findings could trigger a revisit of recommender model design.

Recommendation Systems

Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation

no code implementations28 Sep 2021 Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang song, Chenliang Li

In this paper, we propose a novel concept-aware denoising graph neural network (named CONDE) for micro-video recommendation.

Denoising Recommendation Systems

Grid-VLP: Revisiting Grid Features for Vision-Language Pre-training

no code implementations21 Aug 2021 Ming Yan, Haiyang Xu, Chenliang Li, Bin Bi, Junfeng Tian, Min Gui, Wei Wang

Existing approaches to vision-language pre-training (VLP) heavily rely on an object detector based on bounding boxes (regions), where salient objects are first detected from images and then a Transformer-based model is used for cross-modal fusion.

object-detection Object Detection

Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction

no code implementations ACL 2021 Chenliang Li, Bin Bi, Ming Yan, Wei Wang, Songfang Huang

This work focuses on generative QA which aims to generate an abstractive answer to a given question instead of extracting an answer span from a provided passage.

Generative Question Answering Machine Reading Comprehension

Path-based Deep Network for Candidate Item Matching in Recommenders

no code implementations18 May 2021 Houyi Li, Zhihong Chen, Chenliang Li, Rong Xiao, Hongbo Deng, Peng Zhang, Yongchao Liu, Haihong Tang

PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.

Recommendation Systems Retrieval

SemVLP: Vision-Language Pre-training by Aligning Semantics at Multiple Levels

no code implementations14 Mar 2021 Chenliang Li, Ming Yan, Haiyang Xu, Fuli Luo, Wei Wang, Bin Bi, Songfang Huang

Vision-language pre-training (VLP) on large-scale image-text pairs has recently witnessed rapid progress for learning cross-modal representations.

A Critical Study on Data Leakage in Recommender System Offline Evaluation

1 code implementation21 Oct 2020 Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li

To evaluate recommendation systems in a realistic manner in offline setting, we propose a timeline scheme, which calls for a revisit of the recommendation model design.

Collaborative Filtering Recommendation Systems

A Re-visit of the Popularity Baseline in Recommender Systems

1 code implementation28 May 2020 Yitong Ji, Aixin Sun, Jie Zhang, Chenliang Li

On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system.

Recommendation Systems

CATN: Cross-Domain Recommendation for Cold-Start Users via Aspect Transfer Network

1 code implementation21 May 2020 Cheng Zhao, Chenliang Li, Rong Xiao, Hongbo Deng, Aixin Sun

Given two relevant domains (e. g., Book and Movie), users may have interactions with items in one domain but not in the other domain.

Recommendation Systems

ESAM: Discriminative Domain Adaptation with Non-Displayed Items to Improve Long-Tail Performance

1 code implementation21 May 2020 Zhihong Chen, Rong Xiao, Chenliang Li, Gangfeng Ye, Haochuan Sun, Hongbo Deng

Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items).

Clustering Domain Adaptation +1

PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation

2 code implementations14 Apr 2020 Bin Bi, Chenliang Li, Chen Wu, Ming Yan, Wei Wang, Songfang Huang, Fei Huang, Luo Si

An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.

Abstractive Text Summarization Conversational Response Generation +8

CASE: Context-Aware Semantic Expansion

no code implementations31 Dec 2019 Jialong Han, Aixin Sun, Haisong Zhang, Chenliang Li, Shuming Shi

In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner.

Word Sense Disambiguation

Cross-Domain Recommendation via Preference Propagation GraphNet

no code implementations Conference 2019 Cheng Zhao, Chenliang Li, Cong Fu

We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure.

Link Prediction Transfer Learning

Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders

1 code implementation ACL 2020 Yu Duan, Canwen Xu, Jiaxin Pei, Jialong Han, Chenliang Li

Conditional Text Generation has drawn much attention as a topic of Natural Language Generation (NLG) which provides the possibility for humans to control the properties of generated contents.

Conditional Text Generation

Incorporating External Knowledge into Machine Reading for Generative Question Answering

no code implementations IJCNLP 2019 Bin Bi, Chen Wu, Ming Yan, Wei Wang, Jiangnan Xia, Chenliang Li

Different from existing work on knowledge-aware QA, we focus on a more challenging task of leveraging external knowledge to generate answers in natural language for a given question with context.

Answer Generation Generative Question Answering +1

Exploiting Multiple Embeddings for Chinese Named Entity Recognition

1 code implementation28 Aug 2019 Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li

Identifying the named entities mentioned in text would enrich many semantic applications at the downstream level.

Chinese Named Entity Recognition named-entity-recognition +2

Obj-GloVe: Scene-Based Contextual Object Embedding

no code implementations2 Jul 2019 Canwen Xu, Zhenzhong Chen, Chenliang Li

Recently, with the prevalence of large-scale image dataset, the co-occurrence information among classes becomes rich, calling for a new way to exploit it to facilitate inference.

Dimensionality Reduction Image Generation +2

A Review-Driven Neural Model for Sequential Recommendation

no code implementations1 Jul 2019 Chenliang Li, Xichuan Niu, Xiangyang Luo, Zhenzhong Chen, Cong Quan

Given a sequence of historical purchased items for a user, we devise a novel hierarchical attention over attention mechanism to capture sequential patterns at both union-level and individual-level.

Collaborative Filtering Sequential Recommendation

A Capsule Network for Recommendation and Explaining What You Like and Dislike

1 code implementation1 Jul 2019 Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, Libing Wu

A sentiment capsule architecture with a novel Routing by Bi-Agreement mechanism is proposed to identify the informative logic unit and the sentiment based representations in user-item level for rating prediction.

Targeted Sentiment Analysis: A Data-Driven Categorization

1 code implementation9 May 2019 Jiaxin Pei, Aixin Sun, Chenliang Li

Targeted sentiment analysis (TSA), also known as aspect based sentiment analysis (ABSA), aims at detecting fine-grained sentiment polarity towards targets in a given opinion document.

Aspect-Based Sentiment Analysis (ABSA)

Review-Driven Answer Generation for Product-Related Questions in E-Commerce

1 code implementation27 Apr 2019 Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen

Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation.

Answer Generation

Adversarial Attacks on Deep Learning Models in Natural Language Processing: A Survey

1 code implementation21 Jan 2019 Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, Chenliang Li

In this article, we review research works that address this difference and generatetextual adversarial examples on DNNs.

DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets

no code implementations21 Jan 2019 Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, Donghong Ji

Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems.

Recommendation Systems Representation Learning +2

Enhancing Topic Modeling for Short Texts with Auxiliary Word Embeddings

no code implementations22 Dec 2018 Chenliang Li, Yu Duan, Haoran Wang, Zhiqian Zhang, Aixin Sun, Zongyang Ma

Recent studies show that the Dirichlet Multinomial Mixture (DMM) model is effective for topic inference over short texts by assuming that each piece of short text is generated by a single topic.

text-classification Topic Models +1

Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding

1 code implementation4 Nov 2018 Peifeng Wang, Jialong Han, Chenliang Li, Rong pan

Recent efforts on this issue suggest training a neighborhood aggregator in conjunction with the conventional entity and relation embeddings, which may help embed new entities inductively via their existing neighbors.

Knowledge Graph Embedding World Knowledge

S2SPMN: A Simple and Effective Framework for Response Generation with Relevant Information

no code implementations EMNLP 2018 Jiaxin Pei, Chenliang Li

In this paper, we propose Sequence to Sequence with Prototype Memory Network (S2SPMN) to exploit the relevant information provided by the large dialogue corpus to enhance response generation.

Machine Translation Response Generation +1

A Deep Relevance Model for Zero-Shot Document Filtering

1 code implementation ACL 2018 Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen

In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.

Sentiment Analysis Text Classification +1

Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network

no code implementations NAACL 2018 Chenliang Li, Weiran Xu, Si Li, Sheng Gao

Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.

Abstractive Text Summarization

Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

no code implementations4 Feb 2018 Minh C. Phan, Aixin Sun, Yi Tay, Jialong Han, Chenliang Li

For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected.

Decision Making Entity Disambiguation

Multi-label Dataless Text Classification with Topic Modeling

1 code implementation5 Nov 2017 Daochen Zha, Chenliang Li

With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document.

General Classification Multi-Label Classification +2

NEXT: A Neural Network Framework for Next POI Recommendation

no code implementations15 Apr 2017 Zhiqian Zhang, Chenliang Li, Zhiyong Wu, Aixin Sun, Dengpan Ye, Xiangyang Luo

Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT.

Representation Learning

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