[1]李雪婷,杨 抒,赛亚热·迪力夏提,等.融合内容与协同过滤的混合推荐算法应用研究[J].计算机技术与发展,2021,31(10):24-29.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 005]
　LI Xue-ting,YANG Shu,Saiyare DILIXIATI,et al.Research on Application of Hybrid Recommendation Algorithm of Content Fusion and Collaborative Filtering[J].,2021,31(10):24-29.[doi:10. 3969 / j. issn. 1673-629X. 2021. 10. 005]
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融合内容与协同过滤的混合推荐算法应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年10期
页码:
24-29
栏目:
大数据分析与挖掘
出版日期:
2021-10-10

文章信息/Info

Title:
Research on Application of Hybrid Recommendation Algorithm of Content Fusion and Collaborative Filtering
文章编号:
1673-629X(2021)10-0024-06
作者:
李雪婷1 ; 杨 抒2 ; 赛亚热·迪力夏提1 ; 赵昀杰1
1. 新疆农业大学 计算机与信息工程学院,新疆 乌鲁木齐 830000;
2. 成都大学 计算机学院,四川 成都 610000
Author(s):
LI Xue-ting1 ; YANG Shu2 ; Saiyare DILIXIATI1 ; ZHAO Yun-jie1
1. School of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830000,China;
2. School of Computer,Chengdu University,Chengdu 610000,China
关键词:
混合推荐; 协同过滤; 基于内容; 冷启动问题; 推荐质量
Keywords:
hybrid recommendation; collaborative filtering; content-based; cold start; recommendation quality
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 10. 005
摘要:
随着大数据时代的到来,海量数据出现在人们眼前。 从冗杂的信息中快速获取满足人们个性化需求的数据成为了一个棘手的问题,推荐算法是解决此类问题的有力工具。 针对移动电商平台中信息量较大,人们难以快速获得所需信息的问题,提出了一种融合内容与协同过滤的混合推荐算法。 该算法先利用基于 LFM 的协同过滤算法产生推荐结果;当面临向新用户或新物品进行推荐时,再利用基于内容的推荐算法产生推荐结果。 采用级联的方式将两种推荐算法进行混合,在一定程度上缓解了用户冷启动问题、物品冷启动问题及数据稀疏问题,克服了单一算法的局限性。 实验证明,相较于传统的基于用户和基于物品的推荐算法,该算法能够有效提高推荐结果的准确率、召回率及覆盖率,从而提升推荐质量。
Abstract:
With the advent of the era of big data,massive data appears before people’s eyes. It has become a thorny problem to quickly obtain data from miscellaneous information to meet people’s personalized needs. Recommendation algorithm is a powerful tool to solve such problems. Aiming? ? at the problem that it is difficult for people to get the required information quickly due to the large amount of information in mobile e-commerce platforms,a hybrid recommendation algorithm combining content fusion and collaborative filtering is proposed. Firstly,the collaborative filtering algorithm based on LFM is used to generate the recommendation results. When it comes to making recommendations to new users or new items,content-based recommendation algorithms are used to generate recommendation results. The two recommendation algorithms are mixed? in a cascading way,which alleviate the cold start of users,cold start of items and data sparsity to some extent,and overcome the limitation of single algorithm. Experiment shows that compared with traditional user-based and item - based recommendation algorithms, the proposed algorithm can effectively improve the accuracy rate, recall rate and coverage rate of recommendation results,thus improving the quality of recommendation.

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更新日期/Last Update: 2021-10-10