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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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Abstract  

[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: leeshuqing@163.com

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.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0136     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/124

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
损失
函数
优化器 向量嵌入维度 正则化 激活
函数
潜在
因子数
批量
大小
MSE RMSprop 64 L2 ReLU 32 64
Parameter Setting of Neural Network
Results of MovieLens-100k
Results of MovieLens-Latest-Small
模型 RMSE MAE
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
模型 RMSE MAE
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
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