A User-based Personalized Recommendation Model for Gas Appliances

doi:10.3969/j.issn.1007-7375.2019.06.008

Abstract

Abstract: With the development of e-business, more and more gas appliances are transacted in Internet. Personalized recommendation problem becomes more and more important for individual users with different preferences. The objective of this research is to propose a user-based personalized recommendation model for gas appliances. This recommendation model is based on collaborative filtering algorithm, which is mainly divided into two parts. The first part is to obtain similar users of target user and preferred products of the target user, according to the registration information of target users and the their historical preferences for products. The second part is to formulate the purchase preference evaluation criteria of target users and evaluate the performances of preferred products with respect to evaluation criteria. Finally, experiments are conducted to verify the feasibility and efficiency of the proposed recommendation model.

Key words: gas appliances, collaborative filtering, cold start, personalized recommendation

CLC Number:

TP181

YU Chunxia, LI Nannan. A User-based Personalized Recommendation Model for Gas Appliances[J]. Industrial Engineering Journal, 2019, 22(6): 57-66.

References

[1] 杨燕飞. 中国燃气具行业未来发展趋势综合预测[J]. 家用燃气具, 2013(1):8-11
[2] GOLDBERG D, NICHOLS D, OKI B M, et al. Using collaborative filtering to weave an information tapestry[J]. Communication of the ACM, 1992, 35(12):61-70
[3] 张富国. 基于信任的电子商务个性化推荐关键问题研究[D]. 南昌:江西财经大学, 2009. ZHANG Fuguo. Research on the key issues of trusted-based personalized recommendation systems in e-commerce[D]. Nanchang:Jiangxi University of Finance and Economics, 2009.
[4] BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering[J]. Uncertainty in Artificial Intelligence, 2013, 98(7):43-52
[5] BEI Y, CHEN G, YU L, et al. XML query recommendation based on association rules[C]. Eighth International Association for Computer and Information Science (ACIS) Conference on Software Engineering. USA:IEEE Computer Society, 2007:303-308.
[6] 马丽. 电子商务个性化推荐技术分析及比较[J]. 计算机系统应用, 2008, 17(12):58-61 MA Li. Analysis and comparative study of personalized recommendation techniques in e-commerce[J]. Computer Systems & Applications, 2008, 17(12):58-61
[7] SCHAFER J B, FRANKOWSKI D, HERLOCKER J, et al. Collaborative filtering recommender systems[M]//BRUSILOVSKY P, KOBSA A, NEJDL W. The adaptive web:methods and strategies of web personalization. Berlin:Springer, 2007, 291-324.
[8] 单京晶. 基于内容的个性化推荐系统研究[D]. 长春:东北师范大学, 2015. SHAN Jingjing. Research on content-based personalized recommendation systems[D]. Changchun:Northeast Normal University, 2015.
[9] 巫可, 战荫伟, 李鹰. 融合用户属性的隐语义模型推荐算法[J]. 计算机工程, 2016, 42(12):171-175 WU Ke, ZHAN Yinwei, LI Ying. Recommendation algorithm of latent factor model fused with user attribute[J]. Computer Engineering, 2016, 42(12):171-175
[10] NGUYEN H T H, WISTUBA M, GRABOCKA J, et al. Personalized deep learning for tag recommendation[C]//KIM J, SHIM K, CAO L, et al. Advances in knowledge discovery and data mining. Lecture Notes in Computer Science. Jeju:Springer, 2017:186-197.
[11] ALHAMID M F, RAWASHDEH M, DONG H, et al. Exploring latent preferences for context-aware personalized recommendation systems[J]. IEEE Transactions on Human-Machine Systems, 2016, 46(4):615-623
[12] 朱天宇, 黄振亚, 陈恩红, 等. 基于认知诊断的个性化试题推荐方法[J]. 计算机学报, 2017, 40(1):176-191 ZHU Tianyu, HUANG Zhenya, CHEN Enhong, et al. Cognitive diagnosis based personalized question recommendation[J]. Chinese Journal of Computers, 2017, 40(1):176-191
[13] 朱建勇. 商用燃气炊事用具的现状与发展的思考[J]. 城市公用事业, 2010, 24(4):41-42 ZHU Jianyong. Ponderations over the states-of-the-arts of commercial gas cook appliances and their development[J]. Public Utilities, 2010, 24(4):41-42
[14] 张童杰. 燃气具市场的未来发展趋势——以燃气壁挂炉市场走向为例[J]. 中国商论, 2017(18):6-7
[15] 黄创光, 印鉴, 汪静, 等. 不确定近邻的协同过滤推荐算法[J]. 计算机学报, 2010, 33(8):1369-1377 HUANG Chuangguang, YIN Jian, WANG Jing, et al. Uncertain neighbors' collaborative filtering recommendation algorithm[J]. Chinese Journal of Computers, 2010, 33(8):1369-1377
[16] 嵇晓声, 刘宴兵, 罗来明. 协同过滤中基于用户兴趣度的相似性度量方法[J]. 计算机应用, 2010, 30(10):2618-2620 JI Xiaosheng, LIU Yanbing, LUO Laiming. Similarity measurement based on user interest in collaborative filtering[J]. Journal of Computer Applications, 2010, 30(10):2618-2620
[17] 邓爱林, 朱扬勇, 施伯乐. 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003, 14(9):1621-1628 DENG Ailin, ZHU Yangyong, SHI Bole. A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003, 14(9):1621-1628
[18] 时念云, 葛晓伟, 马力. 基于用户人口统计特征与信任机制的协同推荐[J]. 计算机工程, 2016, 42(6):180-184 SHI Nianyun, Ge Xiaowei, MA Li. Collaborative recommendation based on user demographics and trust mechanism[J]. Computer Engineering, 2016, 42(6):180-184
[19] 吴英俊, 苏宜强, 成乐祥. 基于熵权法和专家打分法的企业节能减排效果评估方法[J]. 电器与能效管理技术, 2015(16):63-68 WU Yingjun, SU Yiqiang, CHENG Lexiang. Energy conservation and emission reduction evaluation for enterprises based on entropy weight coefficient method and expert scoring method[J]. Electrical & Energy Management Technology, 2015(16):63-68
[20] 章穗, 张梅, 迟国泰. 基于熵权法的科学技术评价模型及其实证研究[J]. 管理学报, 2010, 7(1):34-42 ZHANG Sui, ZHANG Mei, CHI Guotai. The science and technology evaluation model based on entropy weight and empirical research during the 10th five-year of China[J]. Chinese Journal of Management, 2010, 7(1):34-42
[21] 杨惠敏, 付萍. 基于熵权的多级模糊综合评价的应用[J]. 华北电力大学学报, 2005, 32(5):106-109 YANG Huimin, FU Ping. Application of fuzzy comprehension evaluation based on entropy weight[J]. Journal of North China Electric Power University, 2005, 32(5):106-109
[22] 隋占丽. 基于协同过滤算法的音乐推荐系统[D]. 福建:华侨大学, 2013. SUI Zhanli. Music recommendation system based on collaborative filtering algorithm[D]. Fujian:Huaqiao Univeristy, 2013.
[23] 查文琴, 梁昌勇, 曹镭. 基于用户聚类的协同过滤推荐方法[J]. 计算机技术与发展, 2009, 19(6):69-71+75 ZHA Wenqin, LIANG Changyong, CAO Lei. Collaborative filtering recommendation Method based on clustering of users[J]. Computer Technology and Development, 2009, 19(6):69-71+75
[24] 李小慧. 基于Jaccard项目类别相似性的个性化推荐算法研究[D]. 长沙:中南大学, 2010. LI Xiaohui. Personalized recommendation algorithm research based on Jaccard item category similarity[D]. Changsha:Central South University, 2010.
[25] 李肖丽. 基于项目相似性的冷启动用户推荐算法[D]. 秦皇岛:燕山大学, 2014. LI Xiaoli. Recommendation algorithm based on item similarity for cold-users[D]. Qinhuangdao:Yanshan University, 2014.
[26] HWANG C L, YOON K. Multiple attribute decision making:methods and applications[M]. New York:Springer-Verlag, 1981.