Volume 32 Issue 1
Jan.  2023
Turn off MathJax
Article Contents
YAN Wenjie, ZHANG Jiahao, LI Ziqi, “An Interactive Perception Method Based Collaborative Rating Prediction Algorithm,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 97-110, 2023, doi: 10.23919/cje.2022.00.034
Citation: YAN Wenjie, ZHANG Jiahao, LI Ziqi, “An Interactive Perception Method Based Collaborative Rating Prediction Algorithm,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 97-110, 2023, doi: 10.23919/cje.2022.00.034

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

doi: 10.23919/cje.2022.00.034
Funds:  This work was supported by the National Natural Science Foundation of China (61702157)
More Information
  • Author Bio:

    Wenjie YAN was born in Hebei Province. He received the Ph.D. degree from the Harbin Institute of Technology. He is currently an Associate Professor with the School of Artificial Intelligence, Hebei University of Technology. His current research interests include deep learning and data mining. (Email: wenjieyanhit@163.com)

    Jiahao ZHANG was born in Shandong Province. He received the M.S. degree with the School of Artificial Intelligence, Hebei University of Technology. His current research interests include deep learning and data mining

    Ziqi LI was born in Tianjin, China. She is currently pursuing her master degree at the School of Artificial Intelligence, Hebei University of Technology. Her current research interests include deep learning and data mining

  • Received Date: 2022-03-05
  • Accepted Date: 2022-06-27
  • Available Online: 2022-07-16
  • Publish Date: 2023-01-05
  • To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF, by fusing dual convolutional autoencoder (DCAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error (RMSE) and mean absolute error (MAE).
  • loading
  • [1]
    Y. Wang, “Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol.17, no.1s, pp.1–25, 2021. doi: 10.1145/3408317
    [2]
    G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol.17, no.6, pp.734–749, 2005. doi: 10.1109/TKDE.2005.99
    [3]
    G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, vol.7, no.1, pp.76–80, 2003. doi: 10.1109/MIC.2003.1167344
    [4]
    P. Covington, J. Adams, and E. Sargin, “Deep neural networks for youtube recommendations,” in Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, pp.191–198, 2016.
    [5]
    G. Zhou, X. Zhu, C. Song, et al., “Deep interest network for click-through rate prediction,” in Proc. of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, pp.1059–1068, 2018.
    [6]
    X. Yu, Q. Peng, L. Xu, et al., “A selective ensemble learning based two-sided cross-domain collaborative filtering algorithm,” Information Processing & Management, vol.58, no.6, article no.102691, 2021. doi: 10.1016/j.ipm.2021.102691
    [7]
    L. Wu, X. He, X. Wang, et al. “A survey on neural recommendation: From collaborative filtering to content and context enriched recommendation,” arXiv preprint, arXiv: 2104.13030, 2021.
    [8]
    L. Chen, Y. Yuan, J. Yang, et al., “Improving the prediction quality in memory-based collaborative filtering using categorical features,” Electronics, vol.10, no.2, article no.214, 2021. doi: 10.3390/electronics10020214
    [9]
    G. R. Lima, C. E. Mello, A. Lyra, et al., “Applying landmarks to enhance memory-based collaborative filtering,” Information Sciences, vol.513, pp.412–428, 2020. doi: 10.1016/j.ins.2019.10.041
    [10]
    Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, vol.42, no.8, pp.30–37, 2009. doi: 10.1109/MC.2009.263
    [11]
    S. Zhang, L. Yao, B. Wu, et al., “Unraveling metric vector spaces with factorization for recommendation,” IEEE Transactions on Industrial Informatics, vol.16, no.2, pp.732–742, 2020. doi: 10.1109/TII.2019.2947112
    [12]
    J. Bobadilla, F. Ortega, A. Gutiérrez, et al., “Classification-based deep neural network architecture for collaborative filtering recommender systems,” International Journal of Interactive Multimedia & Artificial Intelligence, vol.6, no.1, article no.68, 2020. doi: 10.9781/ijimai.2020.02.006
    [13]
    A. Mnih and R. R. Salakhutdinov, “Probabilistic matrix factorization,” in Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07), Vancouver, British Columbia, Canada, pp.1257–1264, 2007.
    [14]
    G. K, Dziugaite and D. M. Roy, “Neural network matrix factorization,” arXiv preprint, arXiv: 1511.06443, 2015.
    [15]
    P. Li, Z. Wang, Z. Ren, et al., “Neural rating regression with abstractive tips generation for recommendation,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan, pp.345–354, 2017.
    [16]
    D. Wu and X. Luo, “Robust latent factor analysis for precise representation of high-dimensional and sparse data,” IEEE/CAA Journal of Automatica Sinica, vol.8, no.4, pp.796–805, 2021. doi: 10.1109/JAS.2020.1003533
    [17]
    Y. Chen, Y. Wang, P. Ren, et al., “Bayesian feature interaction selection for factorization machines,” Artificial Intelligence, vol.302, article no.103589, 2022. doi: 10.1016/j.artint.2021.103589
    [18]
    D. Kim, C. Park, J. Oh, et al., “Convolutional matrix factorization for document context-aware recommendation”, in Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, pp.233–240, 2016.
    [19]
    D. Kim, C. Park, J. Oh, et al., “Deep hybrid recommender systems via exploiting document context and statistics of items,” Information Sciences, vol.417, pp.72–87, 2017. doi: 10.1016/j.ins.2017.06.026
    [20]
    Z. Wang, Y. Zhang, H. Chen, et al., “Deep user modeling for content-based event recommendation in event-based social networks,” in Proceedings of IEEE INFOCOM 2018-IEEE Conference on Computer Communications, Honolulu, Hawaii, USA, pp.1304–1312, 2018.
    [21]
    G. Xu, L. He, and M. Hu, “Document context-aware social recommendation method,” in Proceedings of 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, Hawaii, USA, pp.787–791, 2019.
    [22]
    P. Sun, L. Wu, K. Zhang, et al., “Dual learning for explainable recommendation: Towards unifying user preference prediction and review generation,” in Proceedings of the Web Conference 2020, Taipei, China, pp.837–847, 2020.
    [23]
    Y. Lin, P. Ren, Z. Chen, et al., “Meta matrix factorization for federated rating predictions,” in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Vitrual Event China, pp.981–990, 2020.
    [24]
    L. Yi, S. Ji, L. Ren, R. Su, et al., “A nonlinear feature fusion-based rating prediction algorithm in heterogeneous network,” IEEE Transactions on Computational Social Systems, vol.8, no.3, pp.728–736, 2021. doi: 10.1109/TCSS.2020.3046772
    [25]
    C. Zhang, Y. Wang, L. Zhu, et al., “Multi-graph heterogeneous interaction fusion for social recommendation,” ACM Transactions on Information Systems (TOIS), vol.40, no.2, pp.1–26, 2022. doi: 10.1145/3466641
    [26]
    S. Sedhain, A. K. Menon, S. Sanner, et al., “AutoRec: Autoencoders meet collaborative filtering,” in Proceedings of the 24th International Conference on World Wide Web, Florence, Italy, pp.111–112, 2015.
    [27]
    F. Strub, R. Gaudel, and J. Mary, “Hybrid recommender system based on autoencoders,” in Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, Boston, MA, USA, pp.11–16, 2016.
    [28]
    X. Zhang, H. Liu, X. Chen, et al., “A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness,” Information Sciences, vol.519, pp.306–316, 2020. doi: 10.1016/j.ins.2020.01.044
    [29]
    Z. Khan, N. Iltaf, H. Afzal, et al., “Enriching non-negative matrix factorization with contextual embeddings for recommender systems,” Neurocomputing, vol.380, pp.246–258, 2020. doi: 10.1016/j.neucom.2019.09.080
    [30]
    Q. Wang, S. Li, and G. Chen, “Word-driven and context-aware review modeling for recommendation,” in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, Turin, Italy, pp.1859–1862, 2018.
    [31]
    K. Simonyan and A. Zisserman. “Very deep convolutional networks for large-scale image recognition”, arXiv preprint, arXiv: 1409.1556, 2014.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(6)

    Article Metrics

    Article views (445) PDF downloads(34) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return