Intelligent recommendation of meteorological service based on association rules

Wenfang ZHAO; Yanan LIU; Dongchang YU

doi:10.11959/j.issn.2096-0271.2018020

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Intelligent recommendation of meteorological service based on association rules

APPLICATION | 更新时间：2024-06-03

- Intelligent recommendation of meteorological service based on association rules
- Big data research Vol. 4, Issue 2, Pages: 2018020(2018)
- 作者机构：
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- DOI：10.11959/j.issn.2096-0271.2018020
  CLC： TP183
- Online First：2018-03，
  
  Published：15 March 2018
- 稿件说明：
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Wenfang ZHAO, Yanan LIU, Dongchang YU. Intelligent recommendation of meteorological service based on association rules[J]. Big data research, 2018, 4(2): 2018020.
DOI：

Wenfang ZHAO, Yanan LIU, Dongchang YU. Intelligent recommendation of meteorological service based on association rules[J]. Big data research, 2018, 4(2): 2018020. DOI： 10.11959/j.issn.2096-0271.2018020.

摘要

针对气象公众服务存在的专业化程度不高、针对性不强等问题，提出了一种使用关联规则向用户推荐气象服务产品的方法。将用户访问的Web日志数据经过清洗、转换后进行关联规则挖掘，同时设计了一种基于气象产品浏览总次数计算产品相似度的算法，对挖掘出来的关联规则进行筛选。共进行了5组实验，并对用户浏览产品的行为进行了简单预测。实验结果表明，挖掘出的规则在降水天气和雾霾天气中的正确率比较高，结合气象服务产品的相似度能过滤掉10%的无效规则，从而提升规则的正确性。

Abstract

To overcome the problem in which public meteorological services are rarely optimized for personalization

a recommendation method incorporating improved association rules and collaborative filtering was presented.First

FPGrowth algorithm was applied on pre-processed Web log data to generate association rules.Meanwhile

a customized collaborative filtering algorithm was used to calculate pairwise similarities between meteorological products based on users' browsing records.A total of five experiments were conducted.The experimental results show that the rules are relatively accurate for precipitation and haze weather

because pairwise similarities between meteorological products which are calculated by the proposed algorithm could reduce the total number of invalid or irrelevant association rules by 10%.

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