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基于关联规则的高校图书馆个性化推荐模型

2013年7期

马仲兵(长江师范学院图书馆)

  【摘 要】 利用关联规则对高校图书馆借阅信息进行挖掘,可以找出读者的借阅习惯,从而可以根据读者借阅习惯,能实时、有针对性、主动地为读者提供读者感兴趣的图书。但在实际的运用中,经过统计发现,基于传统的关联规则进行推荐模式存在着关联规则发现困难以及并没有反映出读者借阅习惯的变化问题。论文试图对挖掘出的结果进行分析比较处理,进行特殊的加权处理,从而为用户推荐出命中率高的个性化的推荐模型。实际测试表明:经过加权处理后的关联规则推荐模型能够及时反映出用户的变化,能够满足为用户提供更专业的个性化推荐。

  【关键词】 关联规则;个性化;推荐模型;数据挖掘;高校图书馆

  【Abstracts Using association rules to mine the college library circulation information, you can find out the borrowing habits of readers to provide readers with the books they are interested in. However, in the actual usage, the statistics showed that the recommended mode based on the traditional association rules has some problems that it's difficult to find association rules and it doesn't reflect the change of the readers' reading habits. This paper attempts to recommend a higher hit ratio personalized recommendation model to users by analyzing the dugout results and giving a special weighting disposal. Actual tests show that the association rules given a special weighting disposal make the recommended model to reflect the users' changes in time and are able to meet the need that provides the users with a more professional personalized recommendation.

  【Keywords Association rules; Individuation; Recommendation model; Data mining; University library

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