基于协同过滤推荐算法的公园个性化推荐系统的研建

Park Personalization Recommendation System Based on the Collaborative Filtering Recommendation Algorithm

当今,公园游玩已经是大众日常休闲娱乐的普遍选择,但由于公园种类繁多以及相关信息的冗杂和有效信息的缺乏,游客不易选择适合自己出行计划的公园。随着互联网的迅猛发展,推荐系统已经渗透到了互联网的各个方面,协同过滤技术是目前应用最为广泛和成功的技术。虽然协同过滤推荐技术取得了很大的成功,但传统的算法本身还存在一些问题,包括数据稀疏问题、冷启动性问题,这些问题都是协同过滤必须解决的问题。基于此背景,本文对协同过滤推荐算法中的数据稀疏和冷启动问题做了深入的分析和探讨,并将改进的方案运用到一个具有推荐功能的公园个性化推荐系统中,以解决人们日常的休闲娱乐需求。

Today,more and more people tend to play in the park at their leisure time. But the redundancy of the related information and the lack of effective information usually result in the aimlessness and restrictiveness of people＇s trip. With the rapid development of the internet,recommend system has been penetrated into all parts of the internet,and collaborative filtering is the most widely-used and successful technology currently. However,the traditional algorithm itself has some problems,including scalability problem and coldstart problem which are the problems need to be solved. This paper,based on this background,makes in-depth analysis and exploration in scalability problem and cold-start problem in collaborative filtering recommendation algorithm and applies the improved method to a park personalization recommendation system with a recommend function to solve people＇s daily needs of leisure and entertainment.