Collaborative filtering recommendation based on tags of scenic spots

doi:10.11772/j.issn.1001-9081.2014.10.2854

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2854-2858.DOI: 10.11772/j.issn.1001-9081.2014.10.2854

• Artificial intelligence • Previous Articles Next Articles

Collaborative filtering recommendation based on tags of scenic spots

SHI Yifan¹,WEN Yimin¹,²,CAI Guoyong¹,²,MIU Yuqing¹,²

1. School of Computer Science and Engineering, Guilin University of Electronic Technology, Guilin Guangxi 541004, China;
2. Guangxi Key Laboratory of Trusted Software (Guilin University of Electronic Technology), Guilin Guangxi 541004, China

Received:2014-06-26 Revised:2014-07-08 Online:2014-10-30 Published:2014-10-01
Contact: WEN Yimin

基于景点标签的协同过滤推荐

史一帆¹,文益民¹,²,蔡国永¹,³,缪裕青¹,³

1. 桂林电子科技大学计算机科学与工程学院，广西桂林 541004；
2. 广西可信软件重点实验室（桂林电子科技大学），广西桂林 541004
3.

通讯作者: 文益民
作者简介:史一帆（1988-），女，湖南郴州人，硕士，主要研究方向：机器学习、数据挖掘、旅游信息智能处理；
文益民（1969-），男，湖南益阳人，教授，博士，CCF高级会员，主要研究方向：机器学习、数据挖掘、推荐系统、智慧旅游；
蔡国永（1971-），男，广西河池人，教授，博士，CCF高级会员，主要研究方向：社交媒体分析、可信计算、软件工程；
缪裕青（1966-），女，浙江台州人，副教授，博士，主要研究方向：数据挖掘、云计算、并行计算。
基金资助:
国家自然科学基金资助项目;广西区科学研究与技术开发项目;广西可信软件重点实验室项目

Abstract

Abstract:

In user-based collaborative filtering recommendation based on social relations, sometimes the ratings for the target items can not be predicted. Whats more, in traditional item-based collaborative filtering, there are still some items which are not in the same class with the target item and not suitable to be references for predicting ratings. To handle these problems, two new algorithms of collaborative filtering recommendation were proposed, in which the tags of scenic spots type were introduced to compute the similarity between two scenic spots. The experimental results on the data set of scenic spots ratings show that, compared with the user-based collaborative filtering recommendation algorithms based on social relations, the algorithm based on the social relation and tag can increase the accuracy and the coverage by 10% and 4% respectively, and compared with the item-based collaborative filtering recommendation algorithms, the collaborative filtering recommendation algorithm based on item and tag can increase the accuracy by 15%, it also shows that introducing the tags of scenic spots type can make the computation of the similarity between two scenic spots more accurate.

摘要：

针对基于用户社会关系的协同过滤推荐算法有时无法给出目标用户对目标物品的评分的情况，以及基于物品的协同过滤推荐算法中存在的用户对不同类型物品的评分可能不具有可比性的问题，提出了两个基于物品标签的协同过滤推荐算法。这两个算法在计算物品相似度时引入了物品的类型标签信息。在景点评分数据上的实验结果表明：相比基于用户社会关系的协同过滤推荐算法，基于用户社会关系和物品标签的协同过滤推荐算法的准确率和覆盖率提升最高达10%和4%；相比基于物品的协同过滤推荐算法，基于物品和物品标签的协同过滤推荐算法的准确率提升达15%。这说明景点类型标签信息的引入能使得景点的相似度计算更准确。

CLC Number:

TP391.4
TP18

SHI Yifan WEN Yimin CAI Guoyong MIU Yuqing. Collaborative filtering recommendation based on tags of scenic spots[J]. Journal of Computer Applications, 2014, 34(10): 2854-2858.

史一帆文益民蔡国永缪裕青. 基于景点标签的协同过滤推荐[J]. 计算机应用, 2014, 34(10): 2854-2858.

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Collaborative filtering recommendation based on tags of scenic spots

基于景点标签的协同过滤推荐

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