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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 124-134    DOI: 10.11925/infotech.2096-3467.2021.0136
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Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups
Li Zhenyu,Li Shuqing()
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China
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[Objective] This paper tries to construct a deep collaborative filtering model that can capture local relevance as well as explicit / implicit feedbacks. [Methods] In the explicit recommendation tasks, we embedded similar groups found by implicit feedback search. Then, we create models for user-item group, user-similar-item group, and item-similar-user group with Multi-Layer Perceptron. [Results] We examined the new algorithm with MovieLens datasets. Compared with existing methods, the MAE and RMSE of our model were reduced by 10.94% and 11.79% respectively. [Limitations] More research is needed to identify the optimal number of the nearest neighbors for different datasets. [Conclusions] The new model could more effectively generate the recommendation results.

Key wordsLocal Relevance      Explicit and Implicit Preference      Deep Collaborative Filtering     
Received: 08 February 2021      Published: 23 December 2021
ZTFLH:  TP391  
Fund:Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China(19KJA510011)
Corresponding Authors: Li Shuqing,ORCID:0000-0001-9814-5766     E-mail:

Cite this article:

Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups. Data Analysis and Knowledge Discovery, 2021, 5(11): 124-134.

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Principle of NCF
General Framework of IsMLP
Vector Learning Principle of IsMLP
IsMLP Prediction Model
数据集 用户数量 项目数量 评分数量 稀疏度
MovieLens-100k 943 1 682 100 000 93.7%
MovieLens-Latest-small 610 9 724 100 836 98.3%
Dataset Information
优化器 向量嵌入维度 正则化 激活
MSE RMSprop 64 L2 ReLU 32 64
Parameter Setting of Neural Network
Results of MovieLens-100k
Results of MovieLens-Latest-Small
IsMLP-M 0.915 4 0.717 5
IsMLP-C 0.925 3 0.718 5
KNN-basic 1.021 5 0.806 9
KNN-withMeans 0.957 5 0.753 9
MF 0.948 9 0.740 9
MLP 0.952 7 0.741 3
NeuMF 0.938 1 0.730 3
Comparison of the Best Performance in MovieLens-100k
IsMLP-M 0.866 8 0.660 4
IsMLP-C 0.867 9 0.660 7
KNN-basic 0.973 2 0.748 7
KNN-withMeans 0.902 7 0.690 7
MF 0.887 3 0.674 4
MLP 0.888 0 0.674 9
NeuMF 0.879 7 0.673 3
Comparison of the Best Performance in MovieLens-Latest-Small
Comparison Between IsMLP and EsMLP
[1] Ricci F, Rokach L, Shapira B. Recommender Systems: Introduction and Challenges[A]//Recommender Systems Handbook[M]. Boston, MA: Springer, 2015: 1-34.
[2] Lu J, Wu D S, Mao M S, et al. Recommender System Application Developments: A Survey[J]. Decision Support Systems, 2015, 74:12-32.
doi: 10.1016/j.dss.2015.03.008
[3] Goldberg K, Roeder T, Gupta D, et al. Eigentaste: A Constant Time Collaborative Filtering Algorithm[J]. Information Retrieval, 2001, 4(2):133-151.
doi: 10.1023/A:1011419012209
[4] Koren Y, Bell R, Volinsky C. Matrix Factorization Techniques for Recommender Systems[J]. Computer, 2009, 42(8):30-37.
[5] Xue H J, Dai X Y, Zhang J B, et al. Deep Matrix Factorization Models for Recommender Systems[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017: 3203-3209.
[6] Salakhutdinov R, Mnih A, Hinton G. Restricted Boltzmann Machines for Collaborative Filtering[C]// Proceedings of the 24th International Conference on Machine Learning. 2007: 791-798.
[7] Sedhain S, Menon A K, Sanner S, et al. AutoRec: Autoencoders Meet Collaborative Filtering[C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 111-112.
[8] Liang H Z, Baldwin T. A Probabilistic Rating Auto-Encoder for Personalized Recommender Systems[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015: 1863-1866.
[9] Li S, Kawale J, Fu Y. Deep Collaborative Filtering via Marginalized Denoising Auto-encoder[C]// Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 2015: 811-820.
[10] Ouyang Y X, Liu W Q, Rong W G, et al. Autoencoder-Based Collaborative Filtering[C]// Proceedings of International Conference on Neural Information Processing. Springer, Cham, 2014: 284-291.
[11] Kim D, Park C, Oh J, et al. Convolutional Matrix Factorization for Document Context-Aware Recommendation[C]// Proceedings of the 10th ACM Conference on Recommender Systems. 2016: 233-240.
[12] Singhal A, Sinha P, Pant R. Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works[OL]. arXiv Preprint, arXiv: 1712.07525.
[13] He X N, Liao L Z, Zhang H W, et al. Neural collaborative filtering[C]// Proceedings of the 26th International Conference on World Wide Web. 2017: 173-182.
[14] Zheng Y, Liu C L, Tang B S, et al. Neural Autoregressive Collaborative Filtering for Implicit Feedback[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. 2016: 2-6.
[15] Chen W Y, Cai F, Chen H H, et al. Joint Neural Collaborative Filtering for Recommender Systems[J]. ACM Transactions on Information Systems, 2019, 37(4):1-30.
[16] He X N, Du X, Y Wang X, et al. Outer Product-Based Neural Collaborative Filtering[OL]. arXiv Preprint, arXiv: 1808.03912.
[17] Bai T, Wen J R, Zhang J, et al. A Neural Collaborative Filtering Model with Interaction-Based Neighborhood[C]// Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017: 1979-1982.
[18] Ebesu T, Shen B, Fang Y. Collaborative Memory Network for Recommendation Systems[C]// Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 2018: 515-524.
[19] Deng Z H, Huang L, Wang C D, et al. DeepCF: A Unified Framework of Representation Learning and Matching Function Learning in Recommender System[C]// Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):61-68.
[20] Koren Y. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model[C]// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008: 426-434.
[21] Liu N N, Xiang E W, Zhao M, et al. Unifying Explicit and Implicit Feedback for Collaborative Filtering[C]// Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010: 1445-1448.
[22] Xie R B, Ling C, Wang Y L, et al. Deep Feedback Network for Recommendation[C] // Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020.
[23] Zamani H, Croft W B. Learning a Joint Search and Recommendation Model from User-Item Interactions[C]// Proceedings of the 13th International Conference on Web Search and Data Mining. 2020: 717-725.
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