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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Recommendation Books
Published:
I list some books I have read/am reading that inspired me. Hopefully they can help you too.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
project
新进教师资助计划项目–大语言模型增强基于评论的推荐
基于评论的推荐系统能够吸纳用户的意见，提供高质量、多样化、个性化的推荐结果，在政企服务中起着关键作用。现有方法主要依赖历史评论提取用户偏好，但难以历全面反映目标物品的特点和用户的实际需求，导致推荐结果的准确性和解释性不足。本项目拟围绕用户评价机制开展研究，构建大语言模型增强的基于评论的推荐系统。首先，研究用户在物品方面选择中的关注机制，提出融合方面术语与方面类别的偏 好建模方法，以缓解术语稀疏问题并有效刻画用户的显式偏好特征；其次，研究评论中的讽刺表达机制，提出大语言模型驱动的讽刺检测与语义还原方法，以缓解评论与评分不一致带来的偏差，挖掘用户真实意图；最后，研究用户评论的生成过程，提出结合方面信息与隐含语义的大模型评论生成方法，从生成评论中提取用户对目标物品的需求与物品特征匹配关系。通过上述研究，项目旨在提升基 于评论的推荐系统的准确性、可解释性和鲁棒性。
智能出版赋能全国文化中心建设路径与方式研究
在面对各类文化资源快速增长、用户需求个性化以及文化价值深度开发需求时，传统文化资源存在资源关联不足、传播精准性不足和服务模式相对固化等问题。如何利用人工智能技术重构文化资源组织、传播和服务体系，成为推动文化行业高质量发展的重要课题。本研究围绕“智能出版”，系统分析智能出版发展的技术基础、作用机制及应用路径。研究指出，智能出版依托知识图谱、智能推荐、数字版权保护等关键技术，推动文化资源向“智能知识体系”、 “精准传播”、 “智能服务”转变，并进一步促进文化资源价值由一次性传播向持续性运营演进。面向北京全国文化中心建设需求，研究提出以文化数据新基建为基础，构建智能文化资源底座、智慧文化服务体系和文化价值运营模式，推动北京特色文化资源的智能化开发利用，提升公共文化服务能力和文化产业创新能力。同时，结合北京印刷学院特色优势，提出通过学科交叉融合、复合型人才培养、科研组织创新和社会服务体系建设，形成支撑智能出版发展的高校创新路径。
AI技术对于未来图书发行传播中的作用
This project investigates personalized knowledge organization and intelligent publishing, including demand-driven knowledge selection, adaptive content recommendation, and trustworthy copyright management.
publications
Paper Title Number 1
Journal 1
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).
Paper
A Two-tier Shared Embedding Method for Review-basedRecommender Systems
Recommended citation: Zhen Yang, **Junrui Liu**, Tong Li, Di Wu, Shiqiu Yang, Huan Liu. (2023). Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023: 2928-2938.
Paper | Code
An aspect performance-aware hypergraph neural network for review-based recommendation
Recommended citation: **Junrui Liu**, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang (2025). An aspect performance-aware hypergraph neural network for review-based recommendation; Proceedings of the 18th ACM International Conference on Web Search and Data Mining. 2025.
Paper | Code
A Multi-Factor Collaborative Prediction for Review-based Recommendation
Recommended citation: **Junrui Liu**, Tong Li, Mingliang Yu, Shiqiu Yang, Zifang Tang, Zhen Yang (2025). A Multi-Factor Collaborative Prediction for Review-based Recommendation; Proceedings of the 19th ACM RecSys. 2025, 12-20.
Paper | Code
LSA: A Long-Short-Term Aspect Interest Transformer for Aspect-Based Recommendation
Recommended citation: Le Liu, **Junrui Liu✉**, Yunhan Gao, Ziheng Wang, Tong Li (2025). LSA: A Long-Short-Term Aspect Interest Transformer for Aspect-Based Recommendation; Proceedings of the WISE 2025. Lecture Notes in Computer Science, vol 16368.
Paper
ICAM: Inconsistency-aware Compensation and Adaptive Modeling for Review-Based Recommendation
Recommended citation: Jinglin Zhang, **Junrui Liu✉**, Tong Li, Bo Gao, Zichen Li (2026). ICAM: Inconsistency-aware Compensation and Adaptive Modeling for Review-Based Recommendation; Proceedings of the DependSys 2026.
Paper
SPR: Similarity pairwise ranking for personalized recommendation
Recommended citation: **Junrui Liu**, Zhen Yang, Tong Li, Di Wu, Ruiyi Wang. (2022). "SPR:Similarity pairwise ranking for personalized recommendation." Knowledge-Based Systems. 239:107828.
Paper
FAC: A Music Recommendation Model Based on FusingAudio and Chord Features (115)
Recommended citation: Wette Feng, **Junrui Liu**, Tong Li, Zhen Yang, Di Wu. (2024). "FAC: A Music Recommendation Model Based on FusingAudio and Chord Features (115." International Journal of Software Engineeringand Knowledge Engineer. Vol. 32, Nos. 11 & 12 (2022) 1753–1770.
Paper
Fusion learning of preference and bias from ratings and reviews foritem recommendation
Recommended citation: **Junrui Liu**, Tong Li, Zhen Yang, Di Wu, Huan Liu. (2024). "Fusion learning of preference and bias from ratings and reviews foritem recommendation." Data & Knowledge Engineering. 150:102283.
Paper | Code
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
计算机组成与系统结构
Undergraduate course, BIGC, 2025
数据库原理与应用
Undergraduate course, BIGC, 2026