摘要
【目的】为用户提供旅游景点的个性化推荐,解决因旅游信息过载而导致的用户决策效率下降的问题。【方法】提出基于用户相似度、景点热度和时间上下文的旅游景点个性化推荐算法SPT,并利用从"携程网"获取的真实旅游数据集对比验证了SPT算法和多种传统推荐算法的实际推荐性能。同时本文提出基于"分段用户群"的训练集构建方法,通过实验对比验证了该方法对不同推荐算法性能的影响。【结果】实验结果表明,SPT算法相较于传统推荐算法在准确率(43.38%)、召回率(61.08%)、覆盖率(64.71%)和流行度(3.832)等指标上均表现出更好的性能。利用基于"分段用户群"的方法进一步提高了景点推荐的准确性和有效性,准确率和召回率分别达到43.75%和61.59%。【局限】算法无法为新用户寻找相似用户集,为其推荐基于时间的热门景点列表解决冷启动问题;"分段用户群"方法需进一步在多种不同数据集上检验其适用范围和性能。【结论】所提方法提升了景点推荐效果,有利于提高用户决策效率和满足用户个性化需求。
[Objective] This study tries to provide personalized recommendations for tourists, aiming to improve the low efficiency of user decision-making due to information overload. [Methods] We proposed a new SPT(user Similarity, Popular spot and Time) algorithm, and used real data from Ctrip to compare its recommendation results with traditional algorithms. We also proposed a method to construct training set based on"segmented user groups"and examined its impacts on the recommendation results. [Results] The SPT algorithm yielded better results than traditional recommendation methods in precision, recall, coverage and popularity. The algorithm based on"segmented user groups"further improved the effectiveness of recommendation. The precision and recall of the proposed algorithm reached 43. 75% and 61. 59%. [Limitations] The algorithm could not find similar users for new users. Our new method requires further testing with more datasets. [Conclusions] The proposed method improves recommendation results of tourism attractions, as well as tourists’ decision-making and personalized services.
作者
郑淞尹
谈国新
史中超
Zheng Songyin;Tan Guoxin;Shi Zhongchao(National Research Center of Cultural Industries,Central China Normal University,Wuhan 430079,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第5期92-104,共13页
Data Analysis and Knowledge Discovery
基金
华中师范大学中央高校基本科研业务费重大项目“乡村文化旅游资源开发及评估体系构建研究”(项目编号:CCNU18JCXK06)
湖北省科技创新重大项目“大动漫信息平台关键技术研究与开发应用”(项目编号:2018AAA069)
中央高校基本科研业务费创新资助项目“旅游景点个性化推荐技术研究”(项目编号:2019CXZZ016)的研究成果之一
关键词
景点推荐
分段用户群
时间上下文
协同过滤
个性化旅游
Attractions Recommendation
Segmented User Groups
Time Contexts
Collaborative Filtering
Personalized Tourism
