网购平台搜索功能的质量评估方法研究

Research on Quality Evaluation Approaches for Search Function of Online Shopping Platforms

随着大数据的迅速发展,大数据应用层出不穷,诸如网购零售平台、人脸识别系统、智能决策系统、自助客服、看病导医系统等典型的大数据应用使得人们的生活越发便捷。搜索系统是人们最常使用的大数据应用之一。然而,搜索系统在不同平台上的功能各有侧重,其标准尚且不完善,搜索的质量参次不齐,无法得到保障。与普通的文本搜索引擎相比,网购平台的搜索引擎增加了分类检索、筛选等特色功能,其质量的评价与保障更为复杂。通过对网络零售平台的搜索功能进行研究,针对网购平台搜索功能的质量评价提出了质量参考因素,针对质量因素提出了若干评价指标以及相应的实现算法,并通过实验来论证了质量指标的有效性。

With the rapid development of big data,various big data applications are now in service in different fields.Typical big data applications,such as online shopping and retailing platform,face recognition system,intelligent decision system,self-help service system,medical treatment system make daily life more convenient.Search system is one of the most used big data applications.However,search system varies in different platforms,and there are few standards for it.The quality of search system is hard to assure and validate.Search engine for online shopping systems combines text search and classification-based retrieval comparing to common text search engines.It is harder to validate and evaluate quality of it.Through studying on search system of online shopping platform,some quality factors and relational implementation algorithm were provided in this paper to validate and evaluate shopping system search engines.Experiments were also carried out to assure the correctness of the quality index.