多维度消费人群分析及产品推荐系统

本文将人脸识别、人眼识别和语音识别技术应用到消费人群分析中,提出一款可多维度收集消费人群数据并进行智能产品推荐的系统.区别于传统数据收集的方法,该系统在收集显性评价数据的同时也在收集隐性评价数据,能有效提高数据收集的可信度.系统使用人脸识别获取消费者人脸特征,使用人眼识别跟踪计算人眼停留和注视产品的时长,使用语音识别获取文本情感极性和评价关键词,使用基于用户人脸属性的推荐模型来推荐产品.通过实验得知, 80%的实验者对在经过20位其他实验者进行训练的系统所推出的前5个推荐商品表示满意,这表明,多维度收集消费人群数据能打破传统数据收集的局限性且具有更强的可信度.

Multi-Dimensional Consumer Group Analysis and Product Recommendation System

This study applies facial recognition, human eye recognition, and speech recognition technology to consumer population analysis, and proposes a system that can collect consumer data in multiple dimensions and conduct intelligent product recommendation. Different from traditional data collection method, this system collects implicit evaluation data and collects explicit evaluation data in the meantime, which can effectively increase credibility of collected data. The system exploits on facial recognition to obtain consumers'' facial features, makes use of human eye recognition to track down duration of human eyes staying and gazing at products, uses speech recognition to obtain sentiment polarity of texts and evaluation keywords, and uses a recommendation model based on user face attributes to recommend products. Through the experiments, we discover that 80% of the experimenters express their satisfaction with the first five recommended products introduced by the system, which was formerly trained by 20 other experimenters, showing that multi-dimensional collection of consumer data can break the limits of traditional data collection and possesses a higher credibility.