结合评分和信任的协同推荐算法

秦继伟 1; 3; 郑庆华 1; 郑德立 1; 2; 田锋 1; 2

doi:10.7652/xjtuxb201304017

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结合评分和信任的协同推荐算法

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- 结合评分和信任的协同推荐算法
- A Collaborative Recommendation Algorithm Based on Ratings and Trust
- 西安交通大学学报 2013年47卷第4期页码：100-104+124
- 作者机构：
  
  1. 陕西省天地网技术重点实验室,西安,710049
  2. 西安交通大学计算机科学与技术系,西安,710049
  3. 新疆大学现代教育技术中心,乌鲁木齐,830046
- 作者简介：
- 基金信息：
  
  : 2012-07-10。作者简介: 秦继伟(1978—),女,工程师;郑庆华(通信作者),男,教授,博士生导师。基金项目: 国家杰出青年基金资助项目(60825202);国家自然科学基金资助项目(61070072,61103160);“十一五”国家科技支撑计划重点资助项目(2009BAH51B02)
- DOI：10.7652/xjtuxb201304017
  中图分类号： TP391
- 网络首发：2013-04-10，
  
  纸质出版：2013
- 稿件说明：
移动端阅览
秦继伟 1, 3, 郑庆华 1, 等. 结合评分和信任的协同推荐算法[J]. 西安交通大学学报, 2013,47(4):100-104+124.

A Collaborative Recommendation Algorithm Based on Ratings and Trust[J]. 2013, 47(4): 100-104+124.
秦继伟 1, 3, 郑庆华 1, 等. 结合评分和信任的协同推荐算法[J]. 西安交通大学学报, 2013,47(4):100-104+124. DOI： 10.7652/xjtuxb201304017.

A Collaborative Recommendation Algorithm Based on Ratings and Trust[J]. 2013, 47(4): 100-104+124. DOI： 10.7652/xjtuxb201304017.

摘要

针对现有基于信任的推荐系统虽能缓解冷启动和虚假评价但较难获取用户之间的信任关系

难以建立用户彼此之间的偏好关系的问题

提出了基于评分-信任协同的推荐算法并给出了相关数学表达式和实现流程。该算法充分利用推荐系统中的共同评分

协同用户间的信任关系

有策略地选择用户评分的相似度和用户间信任值

建立用户之间的偏好关系

进而实现推荐。随着共同评分数目下限值的增加带来推荐准确度提高的同时将造成覆盖率的下降

因而关键是选取合适的下限值。实验结果表明

这种混合推荐的方法相比传统协作推荐方法与信任推荐方法

在精度损失极小的情况下

较大地提升了覆盖率。评分覆盖率指标分别提高了3%和32.1%

用户覆盖率指标分别提高了8.2%和15.1%

从而获得了精度与覆盖率的良好平衡。

Abstract

Trust is used for recommendation which can solve the cold start and the cheating rates problem in conventional recommender system

but it is difficult to build the trust network and the preference relations among users. A collaborative recommendation algorithm is proposed based on ratings and trust

and the correlation expressions and the flow of algorithm are also presented. The similarity weight is calculated by the rating value and the trust value. The algorithm couples the ratings with the trust to establish the similarity weight

and the predicted ratings produce the candidate set for the target users. Experimental results and comparisons with the traditional collaborative recommendation and the trust recommendation show that the proposed algorithm greatly improves the coverage with a tiny loss in accuracy. The rating coverage is much higher than that of the traditional collaborative recommendation and the trust recommendation by 3% and 32.1%

respectively

and much higher than the traditional collaborative recommendation and the trust recommendation on the user coverage by 8.2% and 15.1%

respectively. And a perfect balance between the accuracy and the coverage is obtained.

关键词

Keywords

references

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MATSUO Y, YAMAMOTO H. Community gravity: measuring bidirectional effects by trust and rating on online social networks [C]∥Proceedings of the 18th International Conference on World Wide Web. New York, USA: ACM, 2009: 751-760.

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HERLOCKER J L, KONSTAN J A, BORCHERS A, et al. An algorithmic framework for performing collaborative filtering [C]∥Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM, 1999: 230-237.

MASSA P, AVESANI P. Trust-aware bootstrapping of recommender systems [EB/OL]. [2012-06-20] http:∥citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.108.8103rep=rep1type=pdf.

HERLOCKER J L, KONSTAN J A, TERVEEN L G, et al. Evaluating collaborative filtering recommender systems [J]. ACM Transactions on Information Systems, 2004, 22(1): 5-53.

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