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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (11): 28-36    DOI: 10.11925/infotech.2096-3467.2018.0832
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Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O
Yin Cong1, Zhang Liyi2()
1Intellectual Property School, Chongqing University of Technology, Chongqing 400054, China
2School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This paper carries out an in-depth study on context-integrated and personalized recommendation, aiming to address the issue of information overload. [Methods] We proposed a new contextual preference prediction model based on TF-IDF algorithm for post-context filtering, as well as the contextual association probability and universal importance. Then, we adjusted the initial scores of traditional recommendation with the help of item category preferences to generate the final list. [Results] We conducted an empirical study on catering industry and found that the proposed algorithm yielded better results. [Limitations] The accuracy of the context association needs to be improved. [Conclusions] Context information plays an important role in user behavior and decision making. More research is needed to improve the personalized recommendation based on context modeling.

Key wordsContext Information      Contextual Post-Filtering Recommendation      TF-IDF      Contextual Preference      Item Category Preference     
Received: 26 July 2018      Published: 11 December 2018
ZTFLH:  G202  

Cite this article:

Yin Cong,Zhang Liyi. Recommendation Algorithm for Post-Context Filtering Based on TF-IDF: Case Study of Catering O2O. Data Analysis and Knowledge Discovery, 2018, 2(11): 28-36.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.0832     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I11/28

对比项目 传统电子商务环境 O2O环境
数据特性 以用户-项目二维数据为主, 较少考虑用户所处的情境信息;
不受空间限制, 数据为全域数据。
用户-情境-项目多维数据; 地域性极强, 推荐中涉及的数据为本地化数据。
用户偏好 依据浏览行为和购买记录等信息, 忽视情境挖掘用户偏好。 根据用户在不同情境下的偏好, 结合当前情境分析用户当前偏好。
信息匹配 基于“消费者的需求在一定时期稳定不变”这一假设进行信息
匹配。
基于“用户在不同情境下具有不同偏好”这一假设进行信息匹配。
数据稀疏性 数据稀疏性较为严重。 传统推荐稀疏性问题依然存在, 随着维度扩大, 数据稀疏更为严重。
推荐实时性 无需考虑用户情境, 推荐的实时性要求相对较低。 用户需求具有情境敏感性, 需根据用户所处情境变化进行实时推荐。
情境维度 情境实例
位置 武昌、洪山、青山、汉阳、江岸、江汉、硚口
作息 工作日、休息日、节假日
天气 阴、晴、雨、雪、多云
同伴 独自一人、朋友、伴侣、家人
情绪 积极、消极、中性
[1] Borchers A, Herlocker J, Konstan J, et al.Ganging Up on Information Overload[J]. Computer, 1998, 31(4): 106-108.
doi: 10.1109/2.666847
[2] 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. 1999: 230-237.
[3] Bettman J R, Luce M F, Payne J W.Constructive Consumer Choice Processes[J]. Journal of Consumer Research, 1998, 25(3): 187-217.
doi: 10.1086/jcr.1998.25.issue-3
[4] Si H, Kawahara Y, Kurasawa H, et al.A Context-aware Collaborative Filtering Algorithm for Real World Oriented Content Delivery Service[C]//Proceedings of ubiPCMM, 2005. .
[5] Gorgoglione M, Palmisano C, Tuzhilin A.Personalization in Context: Does Context Matter When Building Personalized Customer Models?[C]// Proceedings of the 6th IEEE International Conference on Data Mining, 2006: 222-231.
[6] Mallat N, Rossi M, Tuunainen V K, et al.The Impact of Use Context on Mobile Services Acceptance: The Case of Mobile Ticketing[J]. Information and Management, 2009, 46(3): 190-195.
doi: 10.1016/j.im.2008.11.008
[7] 庄贵军, 周南, 李福安. 情境因素对于顾客购买决策的影响(一个初步的研究)[J]. 数理统计与管理, 2004, 23(4): 7-13.
doi: 10.3969/j.issn.1002-1566.2004.04.002
[7] (Zhuang Guijun, Zhou Nan, Li Fuan.Impact of Situational Factors on Shoppers’ Buying Decision: An Initial Study[J]. Journal of Applied Statistics and Management, 2004, 23(4): 7-13.)
doi: 10.3969/j.issn.1002-1566.2004.04.002
[8] 涂黎. 情境因素对消费者冲动购买行为的影响研究[D]. 武汉: 湖北大学, 2010.
[8] (Tu Li.The Influence of Situational Factors on Impulse Buying Behavior[D]. Wuhan: Hubei University, 2010.)
[9] 胡慕海, 蔡淑琴. 松耦合情境的个性化推荐方法扩展研究[J]. 图书情报工作, 2010, 54(S2): 371-376.
[9] (Hu Muhai, Cai Shuqin.Personalized Recommendation Method on Loosely Coupled Situation with Extended Research[J]. Library and Information Service, 2010, 54(S2): 371-376.
[10] Langseth H, Nielsen T D.A Latent Model for Collaborative Filtering[J]. International Journal of Approximate Reasoning, 2012, 53(4): 447-466.
doi: 10.1016/j.ijar.2011.11.002
[11] 吴颜, 沈洁, 顾天竺, 等. 协同过滤推荐系统中数据稀疏问题的解决[J]. 计算机应用研究, 2007, 24(6): 94-97.
[11] (Wu Yan, Shen Jie, Gu Tianzhu, et al.Algorithm for Sparse Problem in Collaborative Filtering[J]. Application Research of Computers, 2007, 24(6): 94-97.)
[12] Wang J, Zhu Y, Li D, et al.Joint User Attributes and Item Category in Factor Models for Rating Prediction[A]// Zhang Y, Wu J, Zhou C, et al. Multiple-Instance Learning with Evolutionary Instance Selection [M]. Springer International Publishing, 2016: 277-296.
[13] Stefanidis K, Pitoura E, Vassiliadis P.Adding Context to Preferences[C]// Proceedings of the 23rd International Conference on Data Engineering. IEEE, 2007: 846-855.
[14] Stefanidis K, Pitoura E.Fast Contextual Preference Scoring of Database Tuples[C]// Proceedings of the 11th International Conference on Extending Database Technology. 2008: 344-355.
[15] Shin D, Lee J, Yeon J, et al.Context-Aware Recommendation by Aggregating User Context[C]// Proceedings of the 7th IEEE Conference on Commerce and Enterprise Computing. 2009: 423-430.
[16] Jrad Z, Aufure M A, Hadjouni M.A Contextual User Model for Web Personalization[C]// Proceedings of the 2007 International Conference on Web Information Systems Engineering. 2007: 350-361.
[17] Hong J, Suh E H, Kim J, et al.Context-aware System for Proactive Personalized Service Based on Context History[J]. Expert Systems with Applications, 2009, 36(4): 7448-7457.
doi: 10.1016/j.eswa.2008.09.002
[18] Bunningen A H V, Fokkinga M M, Apers P M G, et al. Ranking Query Results Using Context-Aware Preferences[C]// Proceedings of the 23rd International Conference on Data Engineering Workshop. IEEE, 2007: 269-276.
[19] Panniello U, Tuzhilin A, Gorgoglione M, et al.Experimental Comparison of Pre- vs. Post-filtering Approaches in Context-aware Recommender Systems[C]// Proceedings of the 3rd ACM Conference on Recommender Systems. 2009: 265-268.
[20] 闫光辉, 陈勇, 赵红运, 等. 微博个性化信息流推荐研究[J]. 计算机工程与设计, 2014, 35(6): 2013-2016.
doi: 10.3969/j.issn.1000-7024.2014.06.027
[20] (Yan Guanghui, Chen Yong, Zhao Hongyun, et al.Personalized Tweet Recommendation[J]. Computer Engineering and Design, 2014, 35(6): 2013-2016.)
doi: 10.3969/j.issn.1000-7024.2014.06.027
[21] 张新猛, 蒋盛益, 李霞, 等. 基于网络和标签的混合推荐算法[J]. 计算机工程与应用, 2015, 51(1): 119-124.
doi: 10.3778/j.issn.1002-8331.1305-0160
[21] (Zhang Xinmeng, Jiang Shengyi, Li Xia, et al.Hybrid Recommendation Algorithm Based on Network and Tag[J]. Computer Engineering and Applications, 2015, 51(1): 119-124.)
doi: 10.3778/j.issn.1002-8331.1305-0160
[22] 陈功平, 王红. 改进Pearson相关系数的个性化推荐算法[J]. 山东农业大学学报: 自然科学版, 2016, 47(6): 940-944.
[22] (Chen Gongping, Wang Hong.A Personalized Recommendation Algorithm on Improving Pearson Correlation Coefficient[J]. Journal of Shandong Agricultural University: Natural Science Edition, 2016, 47(6): 940-944.)
[23] Wu Y, Ma J.A Genre-based Hybrid Collaborative Filtering Algorithm[J]. Journal of Computational Information Systems, 2014, 10(22): 9831-9838.
[24] 李大学, 谢名亮, 赵学斌. 结合项目类别信息的协同过滤推荐算法[J]. 重庆邮电大学学报: 自然科学版, 2010, 22(6): 823-827.
[24] (Li Daxue, Xie Mingliang, Zhao Xuebin.Collaborative Filtering Recommendation Algorithm Using Item Category Information[J]. Journal of Chongqing University of Posts and Telecommunications: Natural Science Edition, 2010, 22(6): 823-827.)
[25] 潘宇, 林鸿飞. 基于语义极性分析的餐馆评论挖掘[J]. 计算机工程, 2008, 34(17): 208-210.
doi: 10.3969/j.issn.1000-3428.2008.17.074
[25] (Pan Yu, Lin Hongfei.Restaurant Reviews Mining Based on Semantic Polarity Analysis[J]. Computer Engineering, 2008, 34(17): 208-210.)
doi: 10.3969/j.issn.1000-3428.2008.17.074
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