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

被引：0
|
作者：
Yin, Fulian [1 ,2 ]
Feng, Xiaoli [2 ]
Fu, Ruiling [2 ]
Xing, Tongtong [2 ]
Li, Sitong [2 ]
机构：
[1] Commun Univ China, State Key Lab Media Convergence & Commun, Beijing 100024, Peoples R China
[2] Commun Univ China, Coll Informat & Commun Engn, Beijing 100024, Peoples R China
来源：
APPLIED SOFT COMPUTING | 2023年 / 146卷
关键词：
TV program recommendation; Heterogeneous information; Long -short term interest; Neural network;
D O I：
10.1016/j.asoc.2023.110668
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
TV program recommendation can help user find interested programs and improve user experience. The heterogeneous information of programs is important for alleviating the problem of data sparsity. In addition, the existing TV program recommendation methods are lacking in dynamics. This paper proposes a neural TV program recommendation based on dynamic long-short term interest (NPR-DLSTI), which mainly includes two modules: program and user encoder. In the program encoder module, we use convolutional neural network and attention mechanism to learn the heterogeneous information of the program and realize program representation. In the user encoder module, we use gated recurrent unit and personalized attention to learn the dynamic change law of users' interests. Experiments on real data sets show that our method can effectively improve the effectiveness and dynamics of TV program recommendation than other existing models.& COPY; 2023 Elsevier B.V. All rights reserved.
引用
收藏
页数：10
相关论文
共 50 条
  • [31] Forecasting Dynamic Public Transport Origin-Destination Matrices with Long-Short Term Memory Recurrent Neural Networks
    Toque, Florian
    Come, Etienne
    El Mahrsi, Mohamed Khalil
    Oukhellou, Latifa
    2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2016, : 1071 - 1076
  • [32] Fan Fault Diagnosis Based on Long-short Term Memory Network
    Liu, Yao
    Xu, Hai-Ping
    Chu, Ning
    Zheng, Feng
    Wu, Ke-Lin
    Wu, Da-Zhuan
    Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics, 2020, 41 (10): : 2437 - 2445
  • [33] Combining long-term and short-term user interest for personalized hashtag recommendation
    Yu, Jianjun
    Zhu, Tongyu
    FRONTIERS OF COMPUTER SCIENCE, 2015, 9 (04) : 608 - 622
  • [34] Combining long-term and short-term user interest for personalized hashtag recommendation
    Jianjun Yu
    Tongyu Zhu
    Frontiers of Computer Science, 2015, 9 : 608 - 622
  • [35] Combining long-term and short-term user interest for personalized hashtag recommendation
    Jianjun YU
    Tongyu ZHU
    Frontiers of Computer Science, 2015, 9 (04) : 608 - 622
  • [36] Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
    Lee, Woonghee
    Kim, Keonwoo
    Park, Junsep
    Kim, Jinhee
    Kim, Younghoon
    IEEE ACCESS, 2018, 6 : 73068 - 73080
  • [37] Heart sound segmentation via Duration Long-Short Term Memory neural network
    Chen, Yao
    Lv, Jiancheng
    Sun, Yanan
    Jia, Bijue
    APPLIED SOFT COMPUTING, 2020, 95
  • [38] Methodology for the prediction of fluid production in the waterflooding process based on multivariate long-short term memory neural networks
    Rodriguez, Astrid X.
    Salazar, Diego A.
    JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2022, 208
  • [39] Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding
    Yu, Zhibin
    Moirangthem, Dennis S.
    Lee, Minho
    FRONTIERS IN NEUROROBOTICS, 2017, 11
  • [40] DeepEnergy: Prediction of Appliances Energy with Long-Short Term Memory Recurrent Neural Network
    Batbaatar, Erdenebileg
    Park, Hyun Woo
    Li, Dingkun
    Li, Meijing
    Ryu, Keun Ho
    INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2018, PT II, 2018, 10752 : 224 - 234
12345