基于协同过滤算法的微信小程序智能助手
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摘要
随着信息技术和网络的不断迅猛发展,互联网的信息资源急剧增长.信息过载问题促进了个性化推荐技术发展.协同过滤算法通过在用户和信息之间建立联系,被广泛应用于电子商务各个领域.本文提出通过利用微信小程序来获取用户的个性化信息数据,并且通过协同过滤算法,为用户设计的微信小程序智能助手,能够为用户推荐符合用户个性化的生活服务信息.在本文中,介绍了智能助手的设计方法,并详细介绍了系统的功能和个性化推荐功能的实现.
With the rapid development of information technology and network, the information resources of the Internet have increased dramatically. The problem of information overload has promoted the development of customized recommendation technology. Collaborative filtering algorithm is widely used in various fields of e-commerce by establishing links between users and information. In this study, it is proposed that the user's personalized information data can be obtained by using the WeChat mini program, and through the collaborative filtering algorithm, an intelligent assistant designed for users can recommend customized life service information for users. In this paper, the design method of intelligent assistance is introduced, and the realization of system functions and customized recommendation function introduces elaborated in detail.
With the rapid development of information technology and network, the information resources of the Internet have increased dramatically. The problem of information overload has promoted the development of customized recommendation technology. Collaborative filtering algorithm is widely used in various fields of e-commerce by establishing links between users and information. In this study, it is proposed that the user's personalized information data can be obtained by using the WeChat mini program, and through the collaborative filtering algorithm, an intelligent assistant designed for users can recommend customized life service information for users. In this paper, the design method of intelligent assistance is introduced, and the realization of system functions and customized recommendation function introduces elaborated in detail.
引文
1许婉韵.关于微信小程序与原生APP使用偏好性的研究.农家参谋,2018,(21):216-217.
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7邓万宇,刘光达,董莹莹.一种基于Scrapy-Redis的分布式微博数据采集方案.信息技术,2018,(11):59-62.[doi:10.3969/j.issn.1674-2117.2018.11.021]
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9冯宇,李爱萍,段利国.融合社交关系和位置影响的地点推荐算法.计算机工程与设计,2018,39(9):2934-2940.
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11Sun SH,Kong GS,Zhao CW.Collaborative filtering methods based on user relevance degree and weights of recommenditems.Proceedings of 2011 International Conference on Multimedia Technology.Hangzhou,China.2011.12314458.
12王佃来,宿爱霞,安晏辉.基于WebSocket的消息推送系统.计算机系统应用,2017,26(9):87-92.[doi:10.15888/j.cnki.csa.005973]
2Xiao T,Shi YJ.Medical health data analysis based on spark MLlib.International Conference on Artificial Intelligence and Engineering Applications.Hong Kong,China.2016.116-125.
3周绍景,应杰,潘宏斌,等.RESTful架构的应用研究.数字技术与应用,2018,36(5):59-60.
4刘卓,张向利.基于Nginx的负载均衡集群设计与实现.桂林电子科技大学学报,2017,37(6):490-493.[doi:10.3969/j.issn.1673-808X.2017.06.012]
5郭兰杰,梁吉业,赵兴旺.融合社交网络信息的协同过滤推荐算法.模式识别与人工智能,2016,29(3):281-288.
6王晨阳,刘垣,郭李华,等.融合位置相似性度量的快消品电商网站推荐算法.福建工程学院学报,2017,15(6):586-590.[doi:10.3969/j.issn.1672-4348.2017.06.016]
7邓万宇,刘光达,董莹莹.一种基于Scrapy-Redis的分布式微博数据采集方案.信息技术,2018,(11):59-62.[doi:10.3969/j.issn.1674-2117.2018.11.021]
8朱亚兴,余爱民,王夷.基于Redis+MySQL+MongoDB存储架构应用.微型机与应用,2014,33(13):3-5,9.[doi:10.3969/j.issn.1674-7720.2014.13.002]
9冯宇,李爱萍,段利国.融合社交关系和位置影响的地点推荐算法.计算机工程与设计,2018,39(9):2934-2940.
10李晓瑜.协同过滤推荐算法综述.商丘师范学院学报,2018,34(9):7-10.[doi:10.3969/j.issn.1672-3600.2018.09.002]
11Sun SH,Kong GS,Zhao CW.Collaborative filtering methods based on user relevance degree and weights of recommenditems.Proceedings of 2011 International Conference on Multimedia Technology.Hangzhou,China.2011.12314458.
12王佃来,宿爱霞,安晏辉.基于WebSocket的消息推送系统.计算机系统应用,2017,26(9):87-92.[doi:10.15888/j.cnki.csa.005973]