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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

29

期数:

2019年06期

页码:

95-99

栏目:

智能、算法、系统工程

出版日期:

2019-06-10

文章信息/Info

Title:

User’s Age-aware Item-based Collaborative Filtering Recommendation Algorithm

文章编号:

1673-629X(2019)06-0095-05

作者:

张彩廷; 祝永志

曲阜师范大学 信息科学与工程学院,山东 日照 276826

Author(s):

ZHANG Cai-ting; ZHU Yong-zhi

School of Information Science and Engineering,Qufu Normal University,Rizhao 276826,China

关键词:

用户年龄; 实时; Item-based 协同过滤; Spark

Keywords:

user爷’ age; real-time; Item-based collaborative filtering; Spark

分类号:

TP301

DOI:

10. 3969 / j. issn. 1673-629X. 2019. 06. 020

摘要:

随着大数据时代的到来,推荐系统为人们寻找自己感兴趣的物品或事件提供了捷径。 协同过滤推荐算法分为 User-based 协同过滤算法和 Item-based 协同过滤推荐算法。 传统 Item-based 协同过滤推荐算法只关注 Item 间的相似度,与目标用户特征无关,因此传统算法相似度不能有效反映 Item 间的相似程度,推荐准确率低。 并且传统 Item-based 协同过滤算法需要基于所有用户的历史行为数据进行计算,随着数据量的快速增长计算量不断增大,推荐时效性差。 针对以上问题,提出了一种感知用户年龄的 Item-based 协同过滤推荐算法,基于用户年龄特征对用户进行分类,在类内采用加权相似度对 Item 间的相似度进行计算,并且在 Spark 分布式计算平台上运行测试。 实验结果显示,该算法不仅保证了推荐准确率,而且大幅度提高了推荐效率,提升了推荐系统的实时性。

Abstract:

With the advent of the big data era,the recommendation system provides a shortcut for people to find objects or events that they are interested in. Collaborative filtering recommendation algorithms are divided into user-based collaborative filtering algorithm and item-based collaborative filtering recommendation algorithm. The traditional Item-based collaborative filtering recommendation algorithm only pays attention to the similarity between the items and has nothing to do with the characteristics of the target user. And the similarity of traditional algorithms cannot effectively reflect the degree of similarity between items,which leads to inaccurate recommendations. The traditional Item-based collaborative filtering algorithm needs to be calculated based on all users爷 historical behavior data. With the rapid increase of the amount of data,the amount of calculation continues to increase and the recommendation timeliness is poor. For this,we propose a user爷s age-aware item-based collaborative filtering recommendation algorithm. Users are classified based on user age characteristics. The similarity between items is calculated by weighted similarity within the class and the running test is implemented on the Spark distributed computing platform. Experiment shows that the proposed algorithm can greatly improve the recommendation efficiency and the real-time performance of the recommendation system while ensuring the accuracy of the recommendation.

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更新日期/Last Update: 2019-06-10