Towards the next generation of mobile recommender systems

Abstract

Abstract: 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.

Key words: recommender system, computational advertising, mobile sequential recommender

CLC Number:

TP391

SONG Le-yi, XIONG Hui, ZHANG Rong. Towards the next generation of mobile recommender systems[J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 37-45.

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