计算机科学 ›› 2016, Vol. 43 ›› Issue (3): 305-308.doi: 10.11896/j.issn.1002-137X.2016.03.057

• 图形图像与模式识别 • 上一篇    下一篇

基于感知哈希与用户偏好的检索意图建模方法

石宏彬,郭克华   

  1. 中南大学信息科学与工程学院 长沙410083,中南大学信息科学与工程学院 长沙410083;南京理工大学高维信息智能感知与系统教育部重点实验室 南京210094
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(61202341),高维信息智能感知与系统教育部重点实验室创新基金(JYB201502),科技部国家国际科技合作专项项目(2013DFB10070),湖南省创新平台专项(2012GK4106)资助

Retrieval Intention Modeling Based on Perception Hash Algorithm and Browsing Preferences

SHI Hong-bin and GUO Ke-hua   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对商品检索排序问题,提出结合用户查询条件与用户浏览兴趣偏好的排序方法,目的是在不增加用户输入查询条件的前提下,提高用户对商品检索结果的满意度。根据用户提交的查询条件,对数据库中的商品进行筛选和初步排序。在此基础上,以用户的浏览行为分析用户对商品的兴趣浓度,并从用户的历史浏览记录中提取出用户的兴趣偏好模型,计算商品属性信息与用户偏好模型之间的相似度大小,对返回的排序结果进行调整优化。实验表明,基于用户兴趣偏好的排序结果更加符合用户的检索意图。

关键词: 用户偏好,个性化检索,排序算法,电子商务

Abstract: Aiming at the problem of commodity retrieval and ranking,the ranking method based on user’s query request and browsing preferences was proposed.The objective is to improve user’s satisfaction to the retrieval result without increasing input requests.The commodity records from database were selected and ranked primly according to the query requests.Based on this,user’s interest degree was analyzed by their browsing behavior and the user preference model was refined from browsing history.Then the similarity of the attribute information of commodities and preference model was calculated,and the ranking result was adjusted.The experimental result shows that the retrieval result taking user’s preference into consideration is more fit to their retrieval intention.

Key words: User preference,Personalized retrieval,Ranking algorithm,E-commerce

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