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2020 – today
- 2024
- [j44]Yi-Wei Cheng, Zhiqiang Zhong, Jun Pang, Cheng-Te Li:
Hierarchical Bipartite Graph Convolutional Network for Recommendation. IEEE Comput. Intell. Mag. 19(2): 49-60 (2024) - [j43]Feng Chen, Shuhan Yuan, Jilin Hu, Cheng-Te Li, Rudy Raymond, Lidan Shou:
Editorial: Rising stars in data mining and management 2022. Frontiers Big Data 6 (2024) - [j42]Chao-Min Chang, Cheng-Te Li, Shou-De Lin:
Unilateral boundary time series forecasting. Frontiers Big Data 7 (2024) - [j41]Yu-Tung Pai, Nien-En Sun, Cheng-Te Li, Shou-de Lin:
Incremental Data Drifting: Evaluation Metrics, Data Generation, and Approach Comparison. ACM Trans. Intell. Syst. Technol. 15(4): 71:1-71:26 (2024) - [j40]Wen-Ming Zhuang, Chih-Yao Chen, Cheng-Te Li:
Towards Robust Rumor Detection with Graph Contrastive and Curriculum Learning. ACM Trans. Knowl. Discov. Data 18(7): 175 (2024) - [j39]Yi-Ju Lu, Cheng-Te Li:
Forecasting Urban Sensory Values through Learning Attention-adjusted Graph Spatio-temporal Networks. ACM Trans. Spatial Algorithms Syst. 10(1): 4:1-4:22 (2024) - [c119]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Multi-Grained Semantics-Aware Graph Neural Networks (Extended abstract). ICDE 2024: 5691-5692 - [c118]Pei-Cheng Li, Cheng-Te Li:
TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media. PAKDD (6) 2024: 134-146 - [c117]Hsin-Yu Chen, Cheng-Te Li, Ting-Yu Chen:
Skewness-aware Boosting Regression Trees for Customer Contribution Prediction in Financial Precision Marketing. WWW (Companion Volume) 2024: 461-470 - [c116]Yen-Wen Lu, Chih-Yao Chen, Cheng-Te Li:
Dual Graph Networks with Synthetic Oversampling for Imbalanced Rumor Detection on Social Media. WWW (Companion Volume) 2024: 750-753 - [c115]Yen-Wen Lu, Yu-Che Tsai, Cheng-Te Li:
Burstiness-aware Bipartite Graph Neural Networks for Fraudulent User Detection on Rating Platforms. WWW (Companion Volume) 2024: 834-837 - [c114]Cheng-Te Li, Lun-Wei Ku, Yu-Che Tsai:
The 12th International Workshop on Natural Language Processing for Social Media (SocialNLP 2024). WWW (Companion Volume) 2024: 1647-1648 - [c113]Sheng-Fang Yang, Cheng-Te Li:
Detecting Illicit Food Factories from Chemical Declaration Data via Graph-aware Self-supervised Contrastive Anomaly Ranking. WWW 2024: 4501-4511 - [i22]Cheng-Te Li, Yu-Che Tsai, Chih-Yao Chen, Jay Chiehen Liao:
Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions. CoRR abs/2401.02143 (2024) - [i21]Chien-Kun Huang, Yi-Ting Chang, Lun-Wei Ku, Cheng-Te Li, Hong-Han Shuai:
SocialNLP Fake-EmoReact 2021 Challenge Overview: Predicting Fake Tweets from Their Replies and GIFs. CoRR abs/2406.04368 (2024) - 2023
- [j38]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Hierarchical message-passing graph neural networks. Data Min. Knowl. Discov. 37(1): 381-408 (2023) - [j37]Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee:
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification. ACM Trans. Intell. Syst. Technol. 14(5): 88:1-88:22 (2023) - [j36]Chu-Chen Li, Cheng-Te Li, Shou-De Lin:
Learning Privacy-Preserving Embeddings for Image Data to Be Published. ACM Trans. Intell. Syst. Technol. 14(6): 105:1-105:26 (2023) - [j35]I-Chung Hsieh, Cheng-Te Li:
CoANE: Modeling Context Co-Occurrence for Attributed Network Embedding. IEEE Trans. Knowl. Data Eng. 35(1): 167-180 (2023) - [j34]Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li:
FinGAT: Financial Graph Attention Networks for Recommending Top-$K$K Profitable Stocks. IEEE Trans. Knowl. Data Eng. 35(1): 469-481 (2023) - [j33]I-Chung Hsieh, Cheng-Te Li:
NetFense: Adversarial Defenses Against Privacy Attacks on Neural Networks for Graph Data. IEEE Trans. Knowl. Data Eng. 35(1): 796-809 (2023) - [j32]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Multi-Grained Semantics-Aware Graph Neural Networks. IEEE Trans. Knowl. Data Eng. 35(7): 7251-7262 (2023) - [c112]Kuan-Chun Chen, Chih-Yao Chen, Cheng-Te Li:
ANTI-Disinformation: An Adversarial Attack and Defense Network Towards Improved Robustness for Disinformation Detection on Social Media. IEEE Big Data 2023: 5476-5484 - [c111]Tzu-Hsuan Yang, Cheng-Te Li:
When Contrastive Learning Meets Graph Unlearning: Graph Contrastive Unlearning for Link Prediction. IEEE Big Data 2023: 6025-6032 - [c110]Hsin-Yu Chen, Cheng-Te Li, Ting-Yu Chen:
Semi-supervised Curriculum Ensemble Learning for Financial Precision Marketing. CIKM 2023: 3773-3777 - [c109]Cayon Liow, Cheng-Te Li, Chun-Pai Yang, Shou-De Lin:
Pseudo Triplet Networks for Classification Tasks with Cross-Source Feature Incompleteness. CIKM 2023: 4079-4083 - [c108]Karandeep Singh, Yu-Che Tsai, Cheng-Te Li, Meeyoung Cha, Shou-De Lin:
GraphFC: Customs Fraud Detection with Label Scarcity. CIKM 2023: 4829-4835 - [c107]Yi-Zhan Xu, Chih-Yao Chen, Cheng-Te Li:
SUVR: A Search-Based Approach to Unsupervised Visual Representation Learning. ICASSP 2023: 1-5 - [c106]Yen-Wen Lu, Cheng-Te Li:
Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms. ICCE-Taiwan 2023: 49-50 - [c105]Cheng-Te Li, Yu-Che Tsai, Jay Chiehen Liao:
Graph Neural Networks for Tabular Data Learning. ICDE 2023: 3589-3592 - [c104]Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu:
Tutorials at The Web Conference 2023. WWW (Companion Volume) 2023: 648-658 - [c103]Cheng-Te Li, Lun-Wei Ku:
SocialNLP'23: 11th International Workshop on Natural Language Processing for Social Media. WWW (Companion Volume) 2023: 994 - [i20]Karandeep Singh, Yu-Che Tsai, Cheng-Te Li, Meeyoung Cha, Shou-De Lin:
GraphFC: Customs Fraud Detection with Label Scarcity. CoRR abs/2305.11377 (2023) - [i19]Yizhan Xu, Chih-Yao Chen, Cheng-Te Li:
SUVR: A Search-based Approach to Unsupervised Visual Representation Learning. CoRR abs/2305.14754 (2023) - [i18]Jay Chiehen Liao, Cheng-Te Li:
TabGSL: Graph Structure Learning for Tabular Data Prediction. CoRR abs/2305.15843 (2023) - [i17]Kuan-Chun Chen, Cheng-Te Li, Kuo-Jung Lee:
DDNAS: Discretized Differentiable Neural Architecture Search for Text Classification. CoRR abs/2307.06005 (2023) - 2022
- [j31]I-Chung Hsieh, Cheng-Te Li:
Toward an Adaptive Skip-Gram Model for Network Representation Learning. IEEE Access 10: 37506-37514 (2022) - [j30]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Personalised meta-path generation for heterogeneous graph neural networks. Data Min. Knowl. Discov. 36(6): 2299-2333 (2022) - [j29]Yu-Tai Lo, Jay Chiehen Liao, Mei-Hua Chen, Chia-Ming Chang, Cheng-Te Li:
Correction to: Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Medical Informatics Decis. Mak. 22(1): 73 (2022) - [j28]Cheng-Te Li, Cheng Hsu, Yang Zhang:
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings. ACM Trans. Intell. Syst. Technol. 13(1): 16:1-16:21 (2022) - [c102]Hsin-Yu Chen, Cheng-Te Li:
Predicting and Analyzing Privacy Settings and Categories for Posts on Social Media. IEEE Big Data 2022: 5692-5697 - [c101]Man-Ho Li, Bo-Yu Chen, Cheng-Te Li:
A Hybird Method with Gravity Model and Nearest-Neighbor Search for Trip Destination Prediction in New Metropolitan Areas. IEEE Big Data 2022: 6553-6560 - [c100]Eng-Shen Tu, Yong-Han Chen, En-Chao Liu, Hao-Yun Keng, Cheng-Te Li:
An Embarrassingly Simple Rule-based Visiting Circulation Approach to Trip Destination Prediction. IEEE Big Data 2022: 6565-6572 - [c99]I-Chung Hsieh, Cheng-Te Li:
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding. ICDE 2022: 1567-1568 - [c98]Yen-Ting Lee, Cheng-Te Li, Shou-De Lin:
Conditional Sentence Rephrasing without Parallel Training Corpus. ICME Workshops 2022: 1 - [c97]Chih-Chun Yang, Cheng-Te Li, Shou-De Lin:
SMITH: A Self-supervised Downstream-Aware Framework for Missing Testing Data Handling. PAKDD (2) 2022: 499-510 - [c96]Shao-Ping Hsiao, Yu-Che Tsai, Cheng-Te Li:
Unsupervised Post-Time Fake Social Message Detection with Recommendation-aware Representation Learning. WWW (Companion Volume) 2022: 232-235 - [c95]Cheng-Te Li, Lun-Wei Ku, Yu-Che Tsai, Wei-Yao Wang:
SocialNLP'22: 10th International Workshop on Natural Language Processing for Social Media. WWW (Companion Volume) 2022: 849-851 - [i16]Cheng-Te Li, Cheng Hsu, Yang Zhang:
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings. CoRR abs/2205.00313 (2022) - 2021
- [j27]Cheng-Te Li, Hsin-Yu Chen, Yang Zhang:
On exploring feature representation learning of items to forecast their rise and fall in social media. J. Intell. Inf. Syst. 56(3): 409-433 (2021) - [j26]Chieh-Cheng Hsia, Cheng-Te Li:
Mining Influential Who-to-Post and When-to-Post Curators on Social Networks. J. Inf. Sci. Eng. 37(4): 935-958 (2021) - [j25]Yu-Tai Lo, Jah Chiehen Liao, Mei-Hua Chen, Chia-Ming Chang, Cheng-Te Li:
Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Medical Informatics Decis. Mak. 21-S(1): 288 (2021) - [j24]Khurshed Ali, Cheng-Te Li, Yi-Shin Chen:
Joint Selection of Influential Users and Locations under Target Region in Location-Based Social Networks. Sensors 21(3): 709 (2021) - [j23]Cheng-Te Li, Wei-Chu Wang:
Learning template-free network embeddings for heterogeneous link prediction. Soft Comput. 25(21): 13425-13435 (2021) - [j22]Chih-Te Lai, Cheng-Te Li, Shou-De Lin:
Deep Energy Factorization Model for Demographic Prediction. ACM Trans. Intell. Syst. Technol. 12(1): 8:1-8:16 (2021) - [c94]Yu-Che Tsai, Cheng-Te Li:
CARE: learning convolutional attentional recurrent embedding for sequential recommendation. ASONAM 2021: 654-660 - [c93]Cheng Hsu, Cheng-Te Li, Diego Sáez-Trumper, Yi-Zhan Hsu:
WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia. IEEE BigData 2021: 427-436 - [c92]Khurshed Ali, Chih-Yu Wang, Mi-Yen Yeh, Cheng-Te Li, Yi-Shin Chen:
NEDRL-CIM: Network Embedding Meets Deep Reinforcement Learning to Tackle Competitive Influence Maximization on Evolving Social Networks. DSAA 2021: 1-9 - [c91]Chih-Yao Chen, Cheng-Te Li:
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning. NAACL-HLT 2021: 3470-3479 - [c90]Cheng Hsu, Cheng-Te Li:
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation. WWW 2021: 2968-2979 - [e6]Lun-Wei Ku, Cheng-Te Li:
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media, SocialNLP@NAACL 2021, Online, June 10, 2021. Association for Computational Linguistics 2021, ISBN 978-1-954085-32-9 [contents] - [i15]Cheng Hsu, Cheng-Te Li:
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation. CoRR abs/2101.12457 (2021) - [i14]Yi-Ju Lu, Cheng-Te Li:
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting. CoRR abs/2101.12465 (2021) - [i13]Chih-Yao Chen, Cheng-Te Li:
ZS-BERT: Towards Zero-Shot Relation Extraction with Attribute Representation Learning. CoRR abs/2104.04697 (2021) - [i12]I-Chung Hsieh, Cheng-Te Li:
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding. CoRR abs/2106.09241 (2021) - [i11]Yi-Ling Hsu, Yu-Che Tsai, Cheng-Te Li:
FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks. CoRR abs/2106.10159 (2021) - [i10]I-Chung Hsieh, Cheng-Te Li:
NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data. CoRR abs/2106.11865 (2021) - [i9]Cheng Hsu, Cheng-Te Li, Diego Sáez-Trumper, Yi-Zhan Hsu:
WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia. CoRR abs/2111.08543 (2021) - 2020
- [j21]Meeyoung Cha, Wei Gao, Cheng-Te Li:
Detecting fake news in social media: an Asia-Pacific perspective. Commun. ACM 63(4): 68-71 (2020) - [j20]Shu-Kai Zhang, Cheng-Te Li, Shou-De Lin:
A joint optimization framework for better community detection based on link prediction in social networks. Knowl. Inf. Syst. 62(11): 4277-4296 (2020) - [c89]Yi-Ju Lu, Cheng-Te Li:
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. ACL 2020: 505-514 - [c88]Ai-Ni Lee, Kuan-Ying Chen, Cheng-Te Li:
ActRec: A Word Embedding-based Approach to Recommend Movie Actors to Match Role Descriptions. ASONAM 2020: 389-392 - [c87]Yizhan Xu, Sungwon Han, Sungwon Park, Meeyoung Cha, Cheng-Te Li:
A Comprehensive and Adversarial Approach to Self-Supervised Representation Learning. IEEE BigData 2020: 709-717 - [c86]Hsin-Yu Chen, Cheng-Te Li:
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media. EMNLP (1) 2020: 2543-2552 - [c85]Yi-Ju Lu, Cheng-Te Li:
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting. ICDM 2020: 1148-1153 - [c84]Chang-Ming Tsai, Cheng-Te Li:
TideFC: Learning Temporal Interaction for Dynamic Embedding via Feature Crossing. ICS 2020: 565-569 - [c83]Sundong Kim, Yu-Che Tsai, Karandeep Singh, Yeonsoo Choi, Etim Ibok, Cheng-Te Li, Meeyoung Cha:
DATE: Dual Attentive Tree-aware Embedding for Customs Fraud Detection. KDD 2020: 2880-2890 - [c82]Cheng-Te Li:
Explainable Detection of Fake News and Cyberbullying on Social Media. WWW (Companion Volume) 2020: 398 - [e5]Lun-Wei Ku, Cheng-Te Li:
Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media, SocialNLP@ACL 2020, Online, July 10, 2020. Association for Computational Linguistics 2020, ISBN 978-1-952148-22-4 [contents] - [i8]Sungwon Han, Yizhan Xu, Sungwon Park, Meeyoung Cha, Cheng-Te Li:
A Comprehensive Approach to Unsupervised Embedding Learning based on AND Algorithm. CoRR abs/2002.12158 (2020) - [i7]Yi-Ju Lu, Cheng-Te Li:
GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media. CoRR abs/2004.11648 (2020) - [i6]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Hierarchical Message-Passing Graph Neural Networks. CoRR abs/2009.03717 (2020) - [i5]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Adaptive Multi-grained Graph Neural Networks. CoRR abs/2010.00238 (2020) - [i4]Hsin-Yu Chen, Cheng-Te Li:
HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media. CoRR abs/2010.04576 (2020) - [i3]Zhiqiang Zhong, Cheng-Te Li, Jun Pang:
Reinforcement Learning Enhanced Heterogeneous Graph Neural Network. CoRR abs/2010.13735 (2020)
2010 – 2019
- 2019
- [j19]Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin:
Towards a more reliable privacy-preserving recommender system. Inf. Sci. 482: 248-265 (2019) - [j18]Bo-Heng Chen, Cheng-Te Li, Kun-Ta Chuang, Jun Pang, Yang Zhang:
An active learning-based approach for location-aware acquaintance inference. Knowl. Inf. Syst. 59(3): 539-569 (2019) - [j17]Jyun-Yu Jiang, Cheng-Te Li:
Who should I invite: predicting event participants for a host user. Knowl. Inf. Syst. 59(3): 629-650 (2019) - [j16]Hsun-Ping Hsieh, Fandel Lin, Cheng-Te Li, Ian En-Hsu Yen, Hsin-Yu Chen:
Temporal popularity prediction of locations for geographical placement of retail stores. Knowl. Inf. Syst. 60(1): 247-273 (2019) - [j15]Cheng-Te Li:
Mentor-spotting: recommending expert mentors to mentees for live trouble-shooting in Codementor. Knowl. Inf. Syst. 61(2): 799-820 (2019) - [j14]Hsun-Ping Hsieh, Cheng-Te Li:
Inferring Online Social Ties from Offline Geographical Activities. ACM Trans. Intell. Syst. Technol. 10(2): 17:1-17:21 (2019) - [j13]Bo-Heng Chen, Cheng-Te Li, Kun-Ta Chuang:
A check-in shielding scheme against acquaintance inference in location-based social networks. World Wide Web 22(6): 2321-2354 (2019) - [c81]Pei-Chi Wang, Cheng-Te Li:
Spotting Terrorists by Learning Behavior-aware Heterogeneous Network Embedding. CIKM 2019: 2097-2100 - [c80]Yi-Chun Chen, Yu-Che Tsai, Cheng-Te Li:
Query Embedding Learning for Context-based Social Search. CIKM 2019: 2441-2444 - [c79]Yu-Che Tsai, Muzhi Guan, Cheng-Te Li, Meeyoung Cha, Yong Li, Yue Wang:
Predicting New Adopters via Socially-Aware Neural Graph Collaborative Filtering. CSoNet 2019: 155-162 - [c78]Sungkyu Park, Cheng-Te Li, Sungwon Han, Cheng Hsu, Sang Won Lee, Meeyoung Cha:
Learning Sleep Quality from Daily Logs. KDD 2019: 2421-2429 - [c77]Yu-Che Tsai, Chih-Yao Chen, Shao-Lun Ma, Pei-Chi Wang, You-Jia Chen, Yu-Chieh Chang, Cheng-Te Li:
FineNet: a joint convolutional and recurrent neural network model to forecast and recommend anomalous financial items. RecSys 2019: 536-537 - [c76]Ming-Han Feng, Chin-Chi Hsu, Cheng-Te Li, Mi-Yen Yeh, Shou-De Lin:
MARINE: Multi-relational Network Embeddings with Relational Proximity and Node Attributes. WWW 2019: 470-479 - 2018
- [j12]Cheng-Te Li, Yu-Jen Lin, Mi-Yen Yeh:
Forecasting participants of information diffusion on social networks with its applications. Inf. Sci. 422: 432-446 (2018) - [j11]Cheng-Te Li, Hsin-Yu Chen, Ren-Hao Chen, Hsun-Ping Hsieh:
On route planning by inferring visiting time, modeling user preferences, and mining representative trip patterns. Knowl. Inf. Syst. 56(3): 581-611 (2018) - [j10]Cheng-Te Li, Shou-De Lin:
Social Flocks: Simulating Crowds to Discover the Connection Between Spatial-Temporal Movements of People and Social Structure. IEEE Trans. Comput. Soc. Syst. 5(1): 33-45 (2018) - [j9]Cheng-Te Li, Chia-Tai Hsu, Man-Kwan Shan:
A Cross-Domain Recommendation Mechanism for Cold-Start Users Based on Partial Least Squares Regression. ACM Trans. Intell. Syst. Technol. 9(6): 67:1-67:26 (2018) - [j8]Cheng-Te Li, Mei-Yuan Huang, Rui Yan:
Team formation with influence maximization for influential event organization on social networks. World Wide Web 21(4): 939-959 (2018) - [c75]Cheng-Te Li, Yi-Chun Chen:
Query Embedding Learning for Context-based Social Search. CIKM Workshops 2018 - [c74]Yiping Song, Cheng-Te Li, Jian-Yun Nie, Ming Zhang, Dongyan Zhao, Rui Yan:
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems. IJCAI 2018: 4382-4388 - [c73]Lo Pang-Yun Ting, Cheng-Te Li, Kun-Ta Chuang:
Predictive Team Formation Analysis via Feature Representation Learning on Social Networks. PAKDD (3) 2018: 790-802 - [c72]Jyun-Yu Jiang, Cheng-Te Li, Yian Chen, Wei Wang:
Identifying Users behind Shared Accounts in Online Streaming Services. SIGIR 2018: 65-74 - [c71]Yang Zhang, Mathias Humbert, Tahleen A. Rahman, Cheng-Te Li, Jun Pang, Michael Backes:
Tagvisor: A Privacy Advisor for Sharing Hashtags. WWW 2018: 287-296 - [c70]Cheng-Te Li, Lun-Wei Ku:
SocialNLP@WWW 2018 Chairs' Welcome & Organization. WWW (Companion Volume) 2018: 1651-1652 - [e4]Lun-Wei Ku, Cheng-Te Li:
Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media, SocialNLP@ACL 2018, Melbourne, Australia, July 20, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-48-3 [contents] - [r2]Cheng-Te Li, Hsun-Ping Hsieh, Tsung-Ting Kuo, Shou-De Lin:
Opinion Diffusion and Analysis on Social Networks. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i2]Yang Zhang, Mathias Humbert, Tahleen A. Rahman, Cheng-Te Li, Jun Pang, Michael Backes:
Tagvisor: A Privacy Advisor for Sharing Hashtags. CoRR abs/1802.04122 (2018) - 2017
- [c69]Hsin-Yu Chen, Cheng-Te Li:
PSEISMIC: A personalized self-exciting point process model for predicting tweet popularity. IEEE BigData 2017: 2710-2713 - [c68]