计算机科学 ›› 2017, Vol. 44 ›› Issue (Z11): 46-50.doi: 10.11896/j.issn.1002-137X.2017.11A.008

• 智能计算 • 上一篇    下一篇

基于消费者行为的点餐推荐算法

丁铛,张志飞,苗夺谦,陈岳峰   

  1. 同济大学计算机科学与技术系 上海201804;同济大学嵌入式系统与服务计算教育部重点实验室 上海201804,同济大学计算机科学与技术系 上海201804;同济大学大数据与网络安全研究中心 上海200092,同济大学计算机科学与技术系 上海201804;同济大学嵌入式系统与服务计算教育部重点实验室 上海201804,同济大学计算机科学与技术系 上海201804
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目(61273304,1),高等学校博士学科点专项科研基金优先发展领域项目(20130072130004)资助

Ordering Recommender Algorithm Based on Consumers’ Behavior

DING Dang, ZHANG Zhi-fei, MIAO Duo-qian and CHEN Yue-feng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 随着电子商务的发展,餐饮行业现有的大多数管理系统落后于消费者和管理人员的需要,一种行之有效的方法是将推荐系统应用于餐饮管理,根据消费者的行为数据为用户点餐提供菜品的推荐。针对推荐系统中的冷启动问题,提出基于消费者行为的点餐推荐算法,设计出频度统计、关联规则和Markov链3个推荐引擎的加权组合推荐系统。在实际餐厅订单数据样本上,所提算法能够取得令人满意的推荐效果,且得到具有普适性的3个推荐引擎的组合权值(0.2167,0.5167,0.2666),同时得到最佳的推荐长度为3。

关键词: 数据挖掘,推荐系统,关联规则,Markov链,餐饮管理

Abstract: With the development of e-commerce,most of the existing catering management system lags consumers and managers’ need.An effective approach is to apply recommendation systems to catering management,and to provide ordering recommendations according to consumers’ behavior data.As for cold start problems that may arise in the recommending process,the ordering recommender system based on consumers’ behavior was proposed,containing three re-commendation engines,which are frequency statistics,association rules and Markov chain .Experiments on ordering data of real restaurants achieve a satisfactory result,and get a weight combination of three recommendation engine:(0.2167,0.5167,0.2666),and the best recommending length under that weight:3.

Key words: Data mining,Recommendation system,Association rules,Markov chain,Catering management

[1] 赵凌云.面向服务的消费者行为分析及推荐模型研究[D].济南:山东师范大学,2014.
[2] ZHANG F,YUAN N J,ZHENG K,et al.Exploiting diningpreference for restaurant recommendation[C]∥International Conference on World Wide Web.2016:725-735.
[3] 许海玲,吴潇,李晓东,等.互联网推荐系统比较研究[J].软件学报,2009,20(2):350-362.
[4] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge & Data Engineering,2005,17(6):734-749.
[5] 马宏伟,张光卫,李鹏.协同过滤推荐算法综述[J].小型微型计算机系统,2009,30(7):1282-1288.
[6] 何明,刘伟世,魏铮.基于信任网络随机游走模型的协同过滤推荐[J].计算机科学,2016,43(6):257-262.
[7] BREESE,JOHN S,HECKERMAN,et al.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Fourteenth Conference on Uncertainty in Artificial Intelligence.2013:43-52.
[8] 林惠珍,杨晨晖,李翠华,等.基于 Markov 链和关联规则的 Web 访问预测模型[J].厦门大学学报(自然科学版),2010,49(4):476-481.
[9] GRY M,HADDAD H.Evaluation of web usage mining approaches for user’s next request prediction[C]∥ACM CIKM International Workshop on Web Information & Data Management.2003:74-81.
[10] ZUCKERMAN I,ALBRECHT D,NICHOLSON A.Predicting user’s requests on the WWW[C]∥International Conference on User Modeling.1999:275-284.
[11] 刑永康,马少平.多 Markov 链用户浏览预测模型[J].计算机学报,2003,26(11):1510-1517.
[12] `ND `Z S,ZSU T M.A Web page prediction model based on click-stream tree representation of user behavior[C]∥ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2003:535-540.
[13] 项亮.推荐系统实践[M].北京:人民邮电出版社 ,2012.
[14] 于嘉.基于MAHOUT的几种推荐算法的组合实现与评测[D].武汉:华中师范大学,2015.

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