Deep Bi-LSTM Networks for Sequential Recommendation

被引:17
|
作者
Zhao, Chuanchuan [1 ]
You, Jinguo [1 ,2 ]
Wen, Xinxian [1 ]
Li, Xiaowu [1 ,2 ]
机构
[1] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650504, Yunnan, Peoples R China
[2] Comp Technol Applicat Key Lab Yunnan Prov, Kunming 650504, Yunnan, Peoples R China
关键词
recommendation systems; interactive sequence; class label; deep bidirectional LSTM; self-attention; item similarity; MATRIX FACTORIZATION; SYSTEMS;
D O I
10.3390/e22080870
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Recent years have seen a surge in approaches that combine deep learning and recommendation systems to capture user preference or item interaction evolution over time. However, the most related work only consider the sequential similarity between the items and neglects the item content feature information and the impact difference of interacted items on the next items. This paper introduces the deep bidirectional long short-term memory (LSTM) and self-attention mechanism into the sequential recommender while fusing the information of item sequences and contents. Specifically, we deal with the issues in a three-pronged attack: the improved item embedding, weight update, and the deep bidirectional LSTM preference learning. First, the user-item sequences are embedded into a low-dimensional item vector space representation via Item2vec, and the class label vectors are concatenated for each embedded item vector. Second, the embedded item vectors learn different impact weights of each item to achieve item awareness via self-attention mechanism; the embedded item vectors and corresponding weights are then fed into the bidirectional LSTM model to learn the user preference vectors. Finally, the top similar items in the preference vector space are evaluated to generate the recommendation list for users. By conducting comprehensive experiments, we demonstrate that our model outperforms the traditional recommendation algorithms on Recall@20 and Mean Reciprocal Rank (MRR@20).
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Turkish lip-reading using Bi-LSTM and deep learning models
    Atila, Uemit
    Sabaz, Furkan
    ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2022, 35
  • [22] Chinese Cyberbullying Detection Using XLNet and Deep Bi-LSTM Hybrid Model
    Chen, Shifeng
    Wang, Jialin
    He, Ketai
    INFORMATION, 2024, 15 (02)
  • [23] Field Data Forecasting Using LSTM and Bi-LSTM Approaches
    Suebsombut, Paweena
    Sekhari, Aicha
    Sureephong, Pradorn
    Belhi, Abdelhak
    Bouras, Abdelaziz
    APPLIED SCIENCES-BASEL, 2021, 11 (24):
  • [24] A Multi-Task Group Bi-LSTM Networks Application on Electrocardiogram Classification
    Lv, Qiu-Jie
    Chen, Hsin-Yi
    Zhong, Wei-Bin
    Wang, Ying-Ying
    Song, Jing-Yan
    Guo, Sai-Di
    Qi, Lian-Xin
    Chen, Calvin Yu-Chian
    IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE, 2020, 8
  • [25] Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks
    Shrestha, Aman
    Li, Haobo
    Le Kernec, Julien
    Fioranelli, Francesco
    IEEE SENSORS JOURNAL, 2020, 20 (22) : 13607 - 13619
  • [26] Fault classification of three phase induction motors using Bi-LSTM networks
    Jeevesh Vanga
    Durga Prabhu Ranimekhala
    Swathi Jonnala
    Jhansi Jamalapuram
    Balaji Gutta
    Srinivasa Rao Gampa
    Amarendra Alluri
    Journal of Electrical Systems and Information Technology, 10 (1)
  • [27] Self-Attention Bi-LSTM Networks for Radar Signal Modulation Recognition
    Wei, Shunjun
    Qu, Qizhe
    Zeng, Xiangfeng
    Liang, Jiadian
    Shi, Jun
    Zhang, Xiaoling
    IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2021, 69 (11) : 5160 - 5172
  • [28] Att-BiL-SL: Attention-Based Bi-LSTM and Sequential LSTM for Describing Video in the Textual Formation
    Ahmed, Shakil
    Saif, A. F. M. Saifuddin
    Hanif, Md Imtiaz
    Shakil, Md Mostofa Nurannabi
    Jaman, Md Mostofa
    Haque, Md Mazid Ul
    Shawkat, Siam Bin
    Hasan, Jahid
    Sonok, Borshan Sarker
    Rahman, Farzad
    Sabbir, Hasan Muhommod
    APPLIED SCIENCES-BASEL, 2022, 12 (01):
  • [29] Stack Bi-LSTM NSCRF for sequence labeling
    Li, Guodong
    Liu, Yupeng
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 32 - 33
  • [30] Network evasion detection with Bi-LSTM model
    Chen, Kehua
    Jia, JingPing
    2018 INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SCIENCE AND APPLICATION TECHNOLOGY, 2019, 1168