Collaborative filtering recommendation algorithm towards intelligent community

Wei Fu; Jun Liu; Yirong Lai

doi:10.3934/dcdss.2019054

Article Contents

2019, Volume 12, Issue 4&5: 811-822. Doi: 10.3934/dcdss.2019054 open access

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Collaborative filtering recommendation algorithm towards intelligent community

Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

* Corresponding author: Wei Fu
* Corresponding author: Wei Fu

Received: August 2017

Revised: January 2018

Published: March 2019

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Abstract

Collaborative filtering recommendation algorithm is a successful and widely used recommendation method in recommender system. In the collaborative filtering recommendation algorithm, the key step is to find the nearest neighbor. Combined with the application scenario of the intelligent community, Pearson Correlation Coefficient is introduced to improve the accuracy of similarity calculation. At the same time, considering that the residents are relatively fixed, the K-means clustering algorithm can be combined with the user-based collaborative filtering recommendation algorithm to improve the sparsity of the matrix and improve the speed of recommendation. Validation results on MovieLens dataset show that the collaborative filtering recommendation algorithm integrating with K-means clustering algorithm and community factors can more effectively predict the actual user rating in the community application scenario, and improve the recommendation accuracy and recommendation speed, compared with the traditional collaborative filtering recommendation algorithm.

Keywords:

Collaborative filtering recommendation algorithm,
K-means clustering algorithm,
intelligent community,
recommender system,
TOP-N preference.

Mathematics Subject Classification: Primary: 62H, 68T; Secondary: 62H30, 68T10, 68T99.

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Figure 1. The design process of the algorithm

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Figure 2. MAE value of the recommendation algorithm

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Figure 3. Precision of recommendation algorithm

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Figure 4. Running time of recommendation algorithm

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