基于粒子群优化的场感知分解机在网络广告点击率预测中的应用

2018 3rd International Conference on Computer and Communication Systems (ICCCS) Pub Date : 2018-04-27 DOI:10.1109/CCOMS.2018.8463219

M. R. Phangtriastu, S. M. Isa

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引用次数: 1

摘要

随着互联网用户的不断增加，网络广告行业也在不断发展壮大。为了提高广告行业的效率，广告点击率的预测模型是必不可少的。本研究将利用粒子群算法(PSO)对现场感知分解机(FFM)的参数进行优化，以提高FFM的精度。在本研究中，比较了FFM和PSO-FFM的准确率和执行时间。实验结果表明，粒子群算法可以提高FFM的性能。

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Optimizing Field-Aware Factorization Machine with Particle Swarm Optimization on Online Ads Click-through Rate Prediction

Online advertising industry is grow larger along with the increasing numbers of internet users. To make ads industry to be more efficient, prediction model for ads' click-through rate is needed. In this research, Field-aware Factorization Machine (FFM) is going to be optimized using Particle Swarm Optimization (PSO) on FFM parameters to increase the accuracy of the FFM. In this research, FFM and PSO-FFM is compared with accuracy and execution time. Our experimental results show PSO can increase FFM performance.

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来源期刊

2018 3rd International Conference on Computer and Communication Systems (ICCCS)

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