摘要
排序学习技术尝试用机器学习的方法解决排序问题,已被深入研究并广泛应用于不同的领域,如信息检索、文本挖掘、个性化推荐、生物医学等.将排序学习融入推荐算法中,研究如何整合大量用户和物品的特征,构建更加贴合用户偏好需求的用户模型,以提高推荐算法的性能和用户满意度,成为基于排序学习推荐算法的主要任务.对近些年基于排序学习的推荐算法研究进展进行综述,并对其问题定义、关键技术、效用评价、应用进展等进行概括、比较和分析.最后,对基于排序学习的推荐算法的未来发展趋势进行探讨和展望.
Learning to rank（L2R） techniques try to solve sorting problems using machine learning methods, and have been well studied and widely used in various fields such as information retrieval, text mining, personalized recommendation, and biomedicine. The main task of L2 R based recommendation algorithms is integrating L2 R techniques into recommendation algorithms, and studying how to organize a large number of users and features of items, build more suitable user models according to user preferences requirements, and improve the performance and user satisfaction of recommendation algorithms. This paper surveys L2 R based recommendation algorithms in recent years, summarizes the problem definition, compares key technologies and analyzes evaluation metrics and their applications. In addition, the paper discusses the future development trend of L2 R based recommendation algorithms.
出处
《软件学报》
EI
CSCD
北大核心
2016年第3期691-713,共23页
Journal of Software
基金
国家自然科学基金(61272268
61103069)
国家重点基础研究发展计划(973)(2014CB340404)
教育部新世纪优秀人才支持计划(NCET-12-0413)
霍英东教育基金会高等院校青年教师基金(142002)
上海市青年科技启明星计划(15QA1403900)~~
关键词
排序学习
推荐算法
机器学习
兴趣模型
个性化服务
learning to rank
recommendation algorithm
machine learning
interest model
personalized service
