图书馆座位推荐算法研究

Research on Library Seat Recommendation Algorithm

为紧跟时代步伐,越来越多的人选择在空闲时间到图书馆学习。为有效利用图书馆的座位资源,提高座位预约过程中的就座效率,文章针对某高校图书馆不同资源场景下的空闲座位与不同用户的需求匹配问题,采用混合推荐算法实现图书馆座位推荐。此算法将基于用户的协同过滤算法与基于内容的推荐算法相结合,通过基于内容的推荐算法得到座位相似度,从而克服了协同过滤算法在面对冷启动情况时的不足,提高了座位推荐的精确性。

In order to keep up the pace and improve skills,more and more people choose to study in the library in their free time.In order to use library seat resources effectively and improve the efficiency of seat reservation in the process of seat reservation,this paper adopts a hybrid recommendation algorithm to achieve library seat recommendation,aiming at the matching problem between free seats under different resource scenarios of a university library and the needs of different users.This algorithm combines the user-based collaborative filtering algorithm with the content-based recommendation algorithm,and obtains the seat similarity through the content-based recommendation algorithm,so as to overcome the shortcomings of the collaborative filtering algorithm in the face of cold start situation and improve the accuracy of seat recommendation.