摘要
针对当前OPAC缺少大众化的图书推荐服务而造成图书推荐精准度低下、资源利用率不高的现状,进行非个性化图书推荐的应用现状与需求分析、技术设计与功能实现的研究。通过设计方案、分析思路、阐述原理及构建数据表并对用户检索及结果选择行为特征及其隐含的关联规则进行知识分析,在此基础上存储、查询行为数据,挖掘出图书的相关度或相似性,实现非个性化图书推荐功能,并通过权重、显示、排序等优化策略来提升用户使用体验,以期提高图书的可发现性,提升图书馆的服务质量。
To solve the problems of poor precision of book recommendation and low resource utilization rate when OPAC lacks popular books recommendation, the paper studies the status quo of non-personalized book recommendation, technical design and potential measures for improvement. It expounds the principle, builds the data tables, and analyzes the correlation between users＇ search behavior and final choices. Based on these statistics, it suggests the system should save and search behavioral data so as to work out the correlation and similarity between books. Through optimization strategy, it could thus improve users＇ experience, enhance the rate of utilization of books in library and advance the library service.
出处
《图书馆杂志》
CSSCI
北大核心
2013年第8期36-41,共6页
Library Journal
作者简介
黎邦群 惠州学院图书馆，副研究馆员。广东516000
