Deep Learning based Collaborative Filtering

MLP

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• Merit
  • User, Item Representation Learning
  • Matching function learning
  • Modeling nonlinear interactions between users and items

ID Embedding

• ID Embedding: Embedding user and item identifiers into a low-dimensional vector space

  

• NCF: ID Embedding based Latent Factor Model
  (Linear and Non-Linear Matching Function Ensemble)

  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S.
    (2017, April).
    Neural collaborative filtering.
    In Proceedings of the 26th international conference on world wide web (pp. 173-182).

History Embedding

• History Embedding: Generate each user and item expressions based on past interaction history

  

  • by aggregating the raw representations of one entity with which the another entity interacted
  • by reducing the dimensionality of the user-item interaction matrix and its transpose
• DMF: History Embedding based Latent Factor Model
  (Representation Learning)
  • Xue, H. J., Dai, X., Zhang, J., Huang, S., & Chen, J.
    (2017, August).
    Deep matrix factorization models for recommender systems.
    In IJCAI (Vol. 17, pp. 3203-3209).
• DeepCF: History Embedding based Latent Factor Model
  (Representation Learning and Matching Function Learning Ensemble)
  • Deng, Z. H., Huang, L., Wang, C. D., Lai, J. H., & Yu, P. S.
    (2019, July).
    Deepcf: A unified framework of representation learning and matching function learning in recommender system.
    In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 61-68).
• J-NCF: History Embedding based Latent Factor Model
  (Representation Learning and Matching Function Learning Serial)
  • Chen, W., Cai, F., Chen, H., & Rijke, M. D.
    (2019).
    Joint neural collaborative filtering for recommender systems.
    ACM Transactions on Information Systems (TOIS), 37(4), 1-30.

Dual Embedding

• Dual Embedding: Use both ID Embedding and History Embedding

  

• DELF: Dual Embedding based Latent Factor Model
  (Ensemble with separate ID embedding and history embedding)
  • Cheng, W., Shen, Y., Zhu, Y., & Huang, L.
    (2018, July).
    DELF: A dual-embedding based deep latent factor model for recommendation.
    In IJCAI (Vol. 18, pp. 3329-3335).
• DNCF: Dual Embedding based Latent Factor Model
  (Ensemble combining ID embedding and history embedding)
  • He, G., Zhao, D., & Ding, L.
    (2021).
    Dual-embedding based neural collaborative filtering for recommender systems.
    arXiv preprint arXiv:2102.02549.

Semi-Dual Embedding

• Semi-Dual Embedding: Apply dual embedding to only one side of the user or item

• DRNet: User Dual Embedding

  • Ji, D., Xiang, Z., & Li, Y.
    (2020).
    Dual relations network for collaborative filtering.
    IEEE Access, 8, 109747-109757.

Distance Embedding

• Distance Embedding: Calculate Similarity through distance, not inner product, outer product, or concatenation

• DDFL: Distance Embedding based Latent Factor Model

  • Shah, S. T. U., Li, J., Guo, Z., Li, G., & Zhou, Q.
    (2020, September).
    DDFL: a deep dual function learning-based model for recommender systems.
    In International Conference on Database Systems for Advanced Applications (pp. 590-606).
    Cham: Springer International Publishing.

AutoEncoder

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• Merit
  • Restore the user-item interaction matrix
  • Dimensionality Reduction of the user-item interaction matrix
  • Feature Extraction
• AutoRec: AutoEncoder Application
  • Sedhain, S., Menon, A. K., Sanner, S., & Xie, L.
    (2015, May).
    Autorec: Autoencoders meet collaborative filtering.
    In Proceedings of the 24th international conference on World Wide Web (pp. 111-112).
• CDAE: Denoising AutoEncoder Application
  • Wu, Y., DuBois, C., Zheng, A. X., & Ester, M.
    (2016, February).
    Collaborative denoising auto-encoders for top-n recommender systems.
    In Proceedings of the ninth ACM international conference on web search and data mining (pp. 153-162).
• VACF: Variational AutoEncoder Application
  • Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T.
    (2018, April).
    Variational autoencoders for collaborative filtering.
    In Proceedings of the 2018 world wide web conference (pp. 689-698).

CNN

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• Merit
  • Modeling interdimensional high-level interactions between users and items
• ConvNCF: Modeling interdimensional high-level interactions
  • He, X., Du, X., Wang, X., Tian, F., Tang, J., & Chua, T. S.
    (2018).
    Outer product-based neural collaborative filtering.
    arXiv preprint arXiv:1808.03912.
• COMET: Modeling interdimensional high-level interactions
  • Lin, Z., Feng, L., Guo, X., Zhang, Y., Yin, R., Kwoh, C. K., & Xu, C.
    (2023).
    Comet: Convolutional dimension interaction for collaborative filtering.
    ACM Transactions on Intelligent Systems and Technology, 14(4), 1-18.

Attention Mechanism

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• Merit
  • Select important information and remove noise
• DACR: History Embedding based Latent Factor Model Assist.
  • Cui, C., Qin, J., & Ren, Q.
    (2022).
    Deep collaborative recommendation algorithm based on attention mechanism.
    Applied Sciences, 12(20), 10594.