下一代移动推荐系统

推荐系统的目的是通过利用用户的评价信息,实现从过载的信息中识别出用户感兴趣的内容.移动环境下的空间数据复杂性较高,并且用户的上下文信息更加模糊,从而使得移动个性化推荐相比于传统领域面临更大的挑战.本文通过介绍传统推荐算法和移动环境下个性化推荐的特性,给出了移动推荐的挑战;在基于GPS信息的出租车线路推荐和旅游包推荐两个移动案例基础上,提出了移动序列推荐问题及基于约束的旅游推荐问题,并给出了相应的解决方案.

Towards the next generation of mobile recommender systems

Recommender systems aim to identify content of interest from overloaded information by exploiting the opinions of a community of users. Due to the complexity of spatial data and the unclear roles of context-aware information, developing personalized recommender systems in mobile and pervasive environments is more challenging than developing recommender systems from traditional domains. This paper introduced classic recommendation techniques and unique features in mobile recommender systems, as well as the challenges in mobile enviroment. Based on two cases, taxi driving route recommendation and personalized travel package recommendation, we formulated the mobile sequential recommendation (MSR) problem and constrained travel recommendation. Finally, we gave a brief solution of the mobile recommender problem respectively.