Huizhi Liang, Elly

• Book chapter

1. Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen (2020):
   IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing. IEEE Access 8: 228598-228604 G1

• Journal Papers

1. Markchom, T., Liang, H., and Ferryman, J. (2023) Scalable and Explainable Visually-Aware Recommender Systems. Knowledge-Based Systems. (PDF) G1, G2
2. Liang, H. and Markchom, T. (2022) TNE: A General Time-aware Network Representation Learning Framework for Temporal Applications. Knowledge-Based Systems. (PDF)  G1
3. Liang, H. (2020) DRprofiling: Deep Reinforcement User Profiling for Recommendations in Heterogenous Information Networks. IEEE TKDE. (PDF) G1
4. Liang, H., Ganeshbabu, G., Thorne, T. (2020). A Dynamic Bayesian Network Approach for Analysing Topic-sentiment Evolution. IEEE ACCESS. (PDF|IEEE) G3
5. Wang, Q., Cui, M., & Liang, H. (2016). Semantic-Aware Blocking for Entity Resolution. IEEE TKDE 2016 28(1): 166-180. ( PDF | IEEE) G2
6. Ramadan, B., Christen, P., Liang, H., Gayler, R. W. (2015). Dynamic Sorted Neighborhood Indexing for Real-Time Entity Resolution. JDIQ 2015, 6(4):15:1-15:29. (PDF | ACM) G2
7. Liang, H., Xu, Y., Li, Y., & Nayak, R. (2012). Personalized Recommender Systems Integrating Social tags and Item Taxonomy. WIAS 2012. Vol.10, No.3, 227-289. (PDF) G1

• Conference Papers

1. Wang, Y., Liang, H., Zhai, B. (2023) Temporal Neighborhood based Self-supervised Pre-training Model for Sleep Stages Classification. ICBBT 2023 Accepted (ACM | PDF)
2. Li, X., Liang, H., Ryder, C., Jones, R., Liu, Z. (2022) Attractiveness Analysis for Health claims on Food Packages. AusDM 2022 (Springer | PDF) 
3. Liang, H., Liu, Z., Markchom, T. (2022) Relation-aware Blocking for Scalable Recommendation Systems. CIKM 2022 (ACM | PDF) G1 G2
4. Li, X., Liang, H. and Nagala, S., Chen, J. (2022) Improving Ultrasound Image Classification With Local Texture Quantisation. ICASSP 2022 (IEEE | PDF | Video)
5. Li, X., Liang, H. and Liu, Z. (2021) Health Claims Unpacked: A toolkit to Enhance the Communication of Health Claims for food. CIKM 2021 (ACM | PDF | Video) G3
6. Markchom, T. and Liang, H. (2021) Augmenting visual information in knowledge graphs for recommendations. In: ACM IUI. (PDF | Video) G1
7. Xu, Z., Wu, J., Xia, Q., Pan, Z., Ren, J. and Liang, H. (2020). Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds. ACM multi-media. (PDF | video) 
8. Wang, Q., Cui, M., & Liang, H. (2016). Semantic-aware blocking for entity resolution. ICDE 2016: 1468-1469.  (PDF | IEEE) G2
9. Liang, H. & Baldwin T. (2015). A Probabilistic Rating Auto-encoder for Personalized Recommender Systems. CIKM 2015: 1863-1866. (PDF | ACM | Poster) G1G2
10. Liang, H., Wang Y., Christen P., & Gayler, R. W. (2014). Noise-Tolerant Approximate Blocking for Dynamic Real-Time Entity Resolution. PAKDD 2014: 449-460. (PDF | Springer) G2
11. Ramadan, B., Christen, P., Liang, H. (2014). Dynamic Sorted Neighborhood Indexing for Real-Time Entity Resolution. ADC 2014: 1-12. (PDF | Springer) G2
12. Liang, H., Du, H., & Wang, Q. (2014). Real-time Collaborative Filtering Recommender Systems. AusDM 2014. (PDF | slides) G1 G2
13. Li, S., Liang, H., and Ramadan, B. (2013). Two Stage Similarity-aware Indexing for Large-scale Real-time Entity Resolution. AusDM 2013. (PDF) G2
14. Liang, H., Xu, Y., Tjondronegoro, D., & Christen, P. (2012). Time-aware Topic Recommendation Based on Micro-blogs. CIKM 2015: 1657-1661. (PDF | ACM) G1
15. Liang, H., Xu, Y., Li, Y., & Nayak, R. (2010). Personalized Recommender System Based on Item Taxonomy and Folksonomy. CIKM 2010: 1641-1644. (PDF | ACM) G1
16. Liang, H., Xu, Y.,  Li, Y., & Nayak, R. (2010). Connecting Users and Items with Weighted Tags for Personalized Item Recommendations. HT 2010: 51-60. (PDF | ACM) G1
17. Bhuiyan, T., Xu, Y., Jøsang, A., & Liang, H. (2010). Developing Trust Networks Based on User Tagging Information for Recommendation Making. WISE 2010: 357-364. (PDF | Springer) G1
18. Liang, H., Xu, Y.,  Li, Y., Nayak, R., & Weng, L. (2009). Personalized Recommender Systems Integrating Social tags and Item Taxonomy. WI 2009: 540-547. (PDF | ACM) G1
19. Liang, H., Xu, Y., Li, Y., & Nayak, R. (2009). Collaborative Filtering Recommender Systems based on Popular Tags. ADCS 2009: 3-10. (PDF) G1
20. Liang, H., Xu, Y., Li, Y., & Nayak, R. (2009). Tag Based Collaborative Filtering for Recommender Systems. RSKT 2009: 666-673. (PDF | Springer) G1

• Workshop Papers

1. Markchom, T., Liang, H., Gitau, J., Liu, Z., Ojha, V., Taylor, L., Bonnici, J., and Alshadadi, A. (2023) UoR-NCL at SemEval-2023 Task 1: Learning Word-Sense and Image Embeddings for Word Sense Disambiguation. In: SemEval-2023. (PDF)
2. Rusnachenko, N., Markchom, T., and Liang, H. (2023) nclu_team at SemEval-2023 Task 6C1 and 6C2: Attention-based Approaches for Large Court Judgement Prediction with Explanation. In: SemEval-2023. (PDF) Ranked the 3rd of Task 6C2.
3. Zhao, J., Wang, Y., Rusnachenko, N., and Liang, H., Legal_try at SemEval-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents. In SemEval-2023. (PDF)
4. Pugazhenthi T., Liang, H. (2022) Improving conversational recommender systems via knowledge graph-based semantic fusion with historical interaction data. In: 2022 IEEE International Conference on Big Data Workshop. (PDF)
5. Markchom, T., Liang, H., and Chen, J. (2022) UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations. In: SemEval-2022. (PDF)
6. Osei-Brefo, E., and Liang, H. (2022). UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with {BERT} for detecting intended sarcasm. In: SemEval-2022. (PDF)
7. Liu, Z., Haines, C. and Liang, H. (2021) UoR at SemEval-2021 task 7: utilizing pre-trained DistilBert model and multi-scale CNN for humor detection. In: SemEval-2021. (PDF)
8. Markchom, T. and Liang, H. (2021) UoR at SemEval-2021 task 4: using pre-trained BERT Token embeddings for question answering of abstract meaning. In: SemEval-2021. (PDF)
9. Osei-Brefo, E., Markchom, T. and Liang, H. (2021) UoR at SemEval-2021 task 12: on crowd annotations: learning with disagreements to optimise crowd truth. In: SemEval-2021. (PDF)
10. Zehao Liu, Emmanuel Osei-Brefo, Siyuan Chen, Huizhi Liang (2020). UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis. SemEval-2020. (PDF) 
11. Thanet Markchom, Bhuvana Dhruva, Chandresh Pravin, Huizhi Liang. (2020) UoR at SemEval-2020 Task 4: Pre-trained Sentence Transformer Models for Commonsense Validation and Explanation. SemEval-2020. (PDF)
12. Chiara Zucco, Huizhi Liang, Giuseppe Di Fatta, Mario Cannataro. (2018): Explainable Sentiment Analysis with Applications in Medicine. BIBM 2018: 1740-1747 G3
13. Fukuda, T., Nishimura, S., Liang, H. (2016) Towards Sentiment Analysis in Elderly Care Facility. JSAI-isAI Workshops 2016: 143-154. (Springer) G3
14. Liang, Z., Liang, H., Nishimura, T., Martell, M. (2016) Quantitative Analysis of Difference between Good and Bad Motion During Coordinated Movements and Its Potential Application to Healthy Ageing. (HAT-MASH 2016, Oral Presentation) G3
15. Xu, S., Liang, H., & Baldwin, T. (2016). UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification. SemEval@NAACL-HLT 2016: 183-189. Ranked the 3rd out of 34 teams world wide. (PDF) G3
16. Hoogeveen, D., Li, Y., Liang, H., Salehi, B., Baldwin, T., Duong, Long. (2016). UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures. SemEval@NAACL-HLT 2016: 851-856. (PDF) G3
17. Liang, H., Fothergill, R., & Baldwin T. (2015). RoseMerry: A Baseline Message-level Sentiment Classification System, SemEval@NAACL-HLT 2015: 551-555. (PDF | ACLWEB) G3
18. Ramadan, B., Christen, P., Liang, H., Gayler, R. W., Hawking, D. (2013). Dynamic Similarity-Aware Inverted Indexing for Real-Time Entity Resolution. PAKDD Workshops, 2013: 47-58. (PDF | Springer) G2
19. Liang, H., Hogan, J., Xu, Y. (2010). Parallel User profiling based on Folksonomy for Large Scaled Recommender Systems-An implementation of Cascading MapReduce. ICDM 2010 workshop: 154-161. (PDF | IEEE) G2
20. Liang, H., Xu, Y., & Li, Y. (2010). Mining Users’ Opinions based on Item Folksonomy and Taxonomy for Personalized Recommender Systems. ICDM 2010 workshop: 1128-1135. (PDF | IEEE) G1
21. Liang, H., Xu, Y., Li, Y., & Nayak, R., Shaw, G. (2010). A Hybrid Recommender System based on Weighted Tags. SDM 2010 workshop. (PDF) G1
22. Liang, H., Xu, Y., Li, Y., & Nayak, R. (2008). Collaborative Filtering Recommender Systems Using Tag Information. WI 2008: 59-62. (PDF | ACM) G1

• Thesis

Huizhi Liang, PhD Thesis, User Profiling Based on Folksonomy Information in Web 2.0 for Personalized Recommendations (2011) (PDF)