Next Web Page Prediction using Genetic Algorithm and Feed Forward Association Rule based on Web-Log Features

doi:10.23940/ijpe.20.01.p2.1018

Abstract

Abstract:

The frequent utilization of websites has captivated numerous researchers, who have sought to upgrade the performance of websites through behavior analysis. Weblog feature concerning web mining is employed in this paper to construct a web page recommendation model. The feed forward counter model (FFC) is presented to effectively determine association rules with a single data iteration technique. Hence, when the recommended model is executed, the time of execution is diminished. The particle swarm optimization (PSO) algorithm is introduced in the work to pick relevant pages from a given user path as the recommended pages. The association rule aids in the work as the fitness value (FV). The actual dataset is acquired from the project tunnel website. The improvement of numerous evaluation parameters, like the precision, coverage, and m-metric, is achieved using the feed forward association rule with PSO for the next page recommendation system.

Key words: association rule mining, particle swarm optimization, prediction model, web page recommendation

Roshan A. Gangurde and Binod Kumar. Next Web Page Prediction using Genetic Algorithm and Feed Forward Association Rule based on Web-Log Features [J]. Int J Performability Eng, 2020, 16(1): 10-18.

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User-ID	Page-ID	Keywords
1	1	'asset', 'management', 'system', 'project' 'php'
1	2	'hospital', 'management', 'system', 'project' 'php'
2	1	'asset', 'management', 'system', 'project' 'php'
2	3	'Bus', 'management', 'system', 'project' 'php'
2	4	'Online', 'airline', 'project'
3	4	'Online', 'airline', 'project'
2	1	'asset', 'management', 'system', 'project' 'php'
2	2	'Hospital', 'management', 'system', 'project' 'php'

Testing dataset size percentage	Proposed work	PASO [10]
20	0.5521	0.3931
30	0.5664	0.4233
40	0.5594	0.4128
50	0.4916	0.3877

Testing dataset size percentage	Proposed work	PASO [10]
20	20.35	22.36
30	27.68	30.52
40	33.23	37.21
50	42.56	47.45

Testing dataset size percentage	Proposed work	PASO [10]
20	0.3458	0.1324
30	0.1152	0.1022
40	0.1138	0.0912
50	0.0919	0.0901

Testing dataset size percentage	Proposed work	PASO [10]
20	0.5248	0.438
30	0.1915	0.1646
40	0.1891	0.1494
50	0.1549	0.1462