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随着互联网与社交网络等技术的发展,个性化信息服务模式正在不断完善,如何快速获得用户的实时信息需求成为个性化信息服务的关键。目前信息需求获取的方式主要是通过用户主动提交信息、个性化调查以及通过数据挖掘等技术来获取用户的行为模式,这些服务策略可能导致信息滞后,从而无法为用户提供准确的信息服务。文章提出基于轨迹聚类的个性化信息服务策略,实时获取用户访问与检索的轨迹数据,结合轨迹聚类算法建立动态的用户检索模型,快速实现实时个性化信息服务。
With the development of technologies such as the Internet and social networks, the personalized information service model is constantly improving. How to quickly obtain the real-time information needs of users becomes the key to personalized information services. At present, the way of acquiring information needs is mainly through user’s initiative to submit information, personal investigation and technology such as data mining to obtain the user’s behavior patterns. These service strategies may result in information lagging and can not provide users with accurate information services. This paper proposes personalized information service strategy based on trajectory clustering, real-time access to user access and retrieval of trajectory data, combined with trajectory clustering algorithm to establish a dynamic user retrieval model, rapid real-time personalized information services.