摘要
了解用户兴趣是为用户提供个性化服务的关键。用户兴趣有短期兴趣和长期兴趣之分,且具有不稳定性。受人工免疫系统的启发,巧妙地将免疫应答过程应用于用户兴趣挖掘。首先将概率与时间相结合,提出"概念时序动态"的概念,以更好地刻画用户在一段时间内对同一兴趣的关注程度;然后基于人工免疫原理,建立抽取兴趣标签的分类器来提取用户兴趣标签;最后针对增量式学习,建立兴趣标签的"概念时序动态",刻画出用户兴趣自首次出现以来受关注的程度,以此为依据来判断兴趣是否存在迁移及遗忘现象,并为每个兴趣标签附上权重。其主要贡献是创造性地将人工免疫原理应用于用户短期兴趣和长期兴趣的挖掘,并具有增量特性,可以很好地体现用户兴趣迁移特征,是一种自然完整的用户兴趣模型。实验结果表明,该学习模型能够很好地发现用户关注的领域,其平均精度和召回率分别达到79.5%和74.4%,是目前最贴近用户的兴趣挖掘模型。
Understanding user interests is the key to personalize service. User's interests can be categorized into shortterm interests and long-term interests, and may evolve over time. Inspired by artificial immune system (AIS), we skillfully employed immune response process in user interests mining. By combining probability with time, we first introduced the concept temporal dynamics to describe the degree that the user pays attention to a particular interest during a specific time interval. Then, based on AIS,we built classifier to extract user interest tags. Finally, directing at incrementally learning user interest, we built concept temporal dynamics for interest tags, which characterize the attention degree since interests first appears,and on this basis judged whether interests have migrated or being forgotten and assigned weights to each interest tag. The main contribution of this paper is that we creatively applied AIS to user short-term interests and long-term interests mining with incremental feature,which can gracefully reflect the migration of user's interests. It is a natural and complete model for learning user interests. Experimental result on practical dataset indicates that the proposed learning model can effectively discover topics that user focuses on,with the average precision and recall of 79.50/40 and 74. 4% respectively,which is the most suitable user interest mining model.
出处
《计算机科学》
CSCD
北大核心
2015年第5期34-41,共8页
Computer Science
基金
国家自然科学基金项目(60903098
60973040)
国家自然科学青年基金项目(61300148)
吉林省重点科技攻关项目(20130206051GX)资助
关键词
短期兴趣
长期兴趣
兴趣遗忘
兴趣迁移
概念时序动态
增量学习
人工免疫系统
Short-term interests, Long-term interests, Interest-forgotten, Interest migration, Concept temporal dynamics, Incremental mining,Artificial immune system