Ruiling Fu

Communication University of China

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Fulian Yin

Communication University of China

Tongtong Xing

Communication University of China

Meiqi Ji

Communication University of China

Yuewei Wu

Communication University of China

Zebin Yao

Communication University of China

Sitong Li

Henan University of Technology

Tongtong Xing

National Institute of Metrology

Xiaoli Feng

Communication University of China

Feng Xiao-li

Communication University of China

Jingxuan Min

Communication University of China

Cooperative Institutions

Communication University of China

Xidian University

Nankai University

Wuhan National Laboratory for Optoelectronics

Huazhong University of Science and Technology

Northwest A&F University

China Social Welfare Foundation

Shanxi Normal University

Beijing University of Posts and Telecommunications

Institute of Electrical Engineering

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Research on Text Summarization Generation Based on LSTM and Attention Mechanism

Meiqi Ji Ruiling Fu Tongtong Xing Fulian Yin

With the development of Internet technology, the phenomenon of text information overload occurs frequently, and automatic text summarization technology has become a research hotspot. However, in real situations, there is no enough data accumulation in many fields, and lack of high-quality labeled summarization data. Therefore, the paper realizes the generation of Chinese text summarization based on LSTM and attention mechanism, by utilizing LSTM to capture semantic features and combining the attention mechanism based on contextual semantics. Experiments show that the text summarization generation model we constructed has an obvious improvement in F1 value compared with other models. It can complete the task of text summarization generation and solve the problem of low-quality text summarization in some fields.

Multi-document summarization

Information Overload

Text generation

10.1109/ispds54097.2021.00048

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Citations (1)

Multipath-guided heterogeneous graph neural networks for sequential recommendation

Computer Speech & Language (2024)

Fulian Yin Tongtong Xing Meiqi Ji Zebin Yao Ruiling Fu

10.1016/j.csl.2024.101642

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Multi-component graph collaborative filtering using auxiliary information for TV program recommendation

Neural Computing and Applications (2023)

Zebin Yao Meiqi Ji Tongtong Xing Ruiling Fu Sitong Li

Component (thermodynamics)

Representation

10.1007/s00521-023-08940-z

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HGER: a heterogeneous information-based recommendation with graph enhanced representation for TV program

Multimedia Tools and Applications (2023)

Fulian Yin Tongtong Xing Zebin Yao Ruiling Fu Sitong Li

Representation

Learning to Rank

10.1007/s11042-023-16315-8

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Multipath-guided heterogeneous graph neural networks for sequential recommendation

Research Square (Research Square) (2023)

Fulian Yin Meiqi Ji Tongtong Xing Zebin Yao Ruiling Fu

Abstract With the explosion of information and users’ changing interest, program sequential recommendation becomes increasingly important for TV program platforms to help their users find interesting programs. Existing sequential recommendation methods mainly focus on modeling user preferences from users’ historical interaction behaviors directly, with insufficient learning about the dynamics of programs and users, while ignoring the rich semantic information from the heterogeneous graph. To address these issues, we propose the multipath-guided heterogeneous graph neural networks for TV program sequential recommendation (MHG-PSR), which can enhance the representations of programs and users through multiple paths in heterogeneous graphs. In our method, the auxiliary information is fused to supplement the semantics of program and user to obtain initial representations. Then, we explore the interactive behaviors of programs and users with temporal and auxiliary information to model the collaborative signals in the heterogeneous graph and extract the users’ dynamic preferences of programs. Extensive experiments on real-world datasets verify the proposed method can effectively improve the performance of TV program sequential recommendation.

10.21203/rs.3.rs-2942419/v1

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Neural TV program recommendation based on dynamic long-short term interest

Applied Soft Computing (2023)

Fulian Yin Feng Xiao-li Ruiling Fu Tongtong Xing Sitong Li

Representation

10.1016/j.asoc.2023.110668

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An interpretable neural network TV program recommendation based on SHAP

International Journal of Machine Learning and Cybernetics (2023)

Fulian Yin Ruiling Fu Xiaoli Feng Tongtong Xing Meiqi Ji

Interpretability

Leverage (statistics)

Representation

Feature (linguistics)

10.1007/s13042-023-01850-5

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Citations (4)

Sequential recommendations based on time series and dynamic interest for TV program

Tongtong Xing Ruiling Fu Jingxuan Min Fulian Yin

Within the background of the rapid development of information technology, cable TV has lost plenty of users under the shock of audio-visual media. While the study of cable TV recommendation technology can be of use to provide users with personalized recommendations. For the problem that the sequence information of cable TV programs is made full use of in the current recommendation technology research, a sequential recommendations model based on time series and dynamic interest (SR-TSDI) is proposed in this paper. It alleviates the sparsity of the data and fully considers the user's dynamic interest change process. Comprehensive experiment results improved by up to 6.09% in the accuracy indicator and 5.50% in the ranking indicator.

Sequence (biology)

Time sequence

10.1117/12.2667261

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Viewing Prediction Based on Hybrid Kernel Model with User Behaviors and Sentiment Analysis

Meiqi Ji Xiaoli Feng Ruiling Fu Fulian Yin

Sentiment Analysis

Kernel (algebra)

10.5220/0011733000003607

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