融合项目标签相似性的协同过滤推荐算法

doi:10.11772/j.issn.1001-9081.2017092238

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1007-1011.DOI: 10.11772/j.issn.1001-9081.2017092238

融合项目标签相似性的协同过滤推荐算法

廖天星, 王玲

西南石油大学计算机科学学院, 成都 610500

收稿日期:2017-09-13 修回日期:2017-11-25 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 王玲
作者简介:廖天星(1992-),女,四川达州人,硕士研究生,主要研究方向:推荐算法、机器学习;王玲(1966-),女,四川眉山人,教授,博士,主要研究方向:智能算法、模式识别。
基金资助:
国家自然科学基金资助项目（41674141）。

Collaborative filtering recommendation algorithm combined with item tag similarity

LIAO Tianxing, WANG Ling

College of Computer Science, Southwest Petroleum University, Chengdu Sichuan 610500, China

Received:2017-09-13 Revised:2017-11-25 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (41674141).

摘要/Abstract

摘要： 针对传统推荐算法在相似性计算和评分预测方法中存在预测精度和稳定性的不足，为进一步提高算法精确度和稳定性，提出一种新的推荐算法。首先，依据各项目的重要标签的数量，计算出项目间M²相似性，依据该相似性构成该项目的邻近项目集；然后，参考Slope One加权算法思想，定义了新的评分预测方法；最后，使用该评分方法基于邻近项目集对用户评分进行预测。为了验证该算法的准确性和稳定性，在MovieLens数据集上与基于曼哈顿距离的K-最近邻（KNN）算法等传统推荐算法进行了对比，实验结果表明该算法与KNN算法相比平均绝对误差下降7.6%，均方根误差下降7.1%，并且在稳定性方面也更好，能更准确地为用户提供个性化推荐。

关键词: 项目相似性, 标签, K近邻, 协同过滤推荐算法

Abstract: Aiming at the shortages in similarity calculation and rating prediction in traditional recommendation system, in order to further improve the accuracy and stability of the algorithm, a new recommendation algorithm was proposed. Firstly, according to the number of important labels for an item, the M² similarity between the item and other items was calculated, which was used to constitute the nearest item set of the item. Then, according to the Slope One weighting theory, a new rating prediction method was designed to predict users' ratings based on the nearest item set. To validate the accuracy and stability of the proposed algorithm, comparison experiments with the traditional recommendation algorithms including K-Nearest Neighbor (KNN) algorithm based on Manhattan distance were conducted on MovieLens dataset. The experimental results showed that compared with the KNN algorithm, the mean absolute error and the root mean square error of the new algorithm were decreased by 7.6% and 7.1% respectively. Besides, the proposed algorithm performs better in stability, which can provide more accurate and personalized recommendation.

Key words: item similarity, label, K-Nearest Neighbor (KNN), collaborative filtering recommendation algorithm

中图分类号:

TP301

廖天星, 王玲. 融合项目标签相似性的协同过滤推荐算法[J]. 计算机应用, 2018, 38(4): 1007-1011.

LIAO Tianxing, WANG Ling. Collaborative filtering recommendation algorithm combined with item tag similarity[J]. Journal of Computer Applications, 2018, 38(4): 1007-1011.

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融合项目标签相似性的协同过滤推荐算法

Collaborative filtering recommendation algorithm combined with item tag similarity

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