Collaborative Filtering Model Fusing Singularity and Diffusion Process

Yang, Xingyao; Yu, Jihong; Turgun, Ibrahim; Liao, Bin; Qian, Yu-Rong

doi:10.3724/sp.j.1001.2013.04350

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Cited by 3 publications

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“…MAE, Coverage, Precision and Recall were used to compare the three models in collaborative filtering model evaluation [8].In this paper,we use precision, recall and F1 indicators.…”

Section: Related Workmentioning

confidence: 99%

Improved Recommendation Sorting of Collaborative Filtering Algorithm

Liao¹,

Sun²,

Ouyang³

2017

Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 201

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Abstract. The traditional collaborative filtering algorithm has no overall quantitative understanding on users' preference. This paper proposes a collaborative filtering algorithm based on improved recommendation sorting. Based on the traditional collaborative filtering rating prediction, three kinds of weighted sorting strategies are proposed to recommendation list, which are based on the combination of users' preference vector and item quality. Experiments on the MovieLens data set show that, in the same rating prediction process, the recommended results of the improved sorting have a significant increase of nearly one time in the precision, recall and F1 indicators.

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“…MAE, Coverage, Precision and Recall were used to compare the three models in collaborative filtering model evaluation [8].In this paper,we use precision, recall and F1 indicators.…”

Section: Related Workmentioning

confidence: 99%