Exploring the Influence of Contexts for Mobile Recommendation

نویسندگان

  • Jun Zeng
  • Feng Li
  • Yinghua Li
  • Junhao Wen
  • Yingbo Wu
چکیده

Withtherapiddevelopmentofmobileinternet,itisdifficulttoobtainhigh-qualityrecommendationin suchacomplicatedmobileenvironment,justdependingontraditionaluser-itembinaryinformation. Howtousemultiplecontextstogeneratesatisfyingrecommendationhasbeenahottopicinsomefields likee-commerce,tourismandnews.Contextawarerecommendersystem(CARS)importscontexts intorecommendertogenerateubiquitousandpersonalizedrecommendation.Inthispaper,thebasic informationofCARS,suchasthedefinitionofcontext,theprocessofCARSandevaluationare introducedcarefully.Inordertoexplorewhethercontextshaveagreatinfluenceonrecommendation ornot,theauthorsconductexperimentsonrealdatasets.Experimentalresultsshowrecommender that incorporates contexts significantly improvesperformanceover the traditional recommender. Finally,StateoftheartaboutCARSisdetailed. KEyWoRdS Context Aware Recommender System, Context Model, Mobile Environment, Personalized Recommender, Social Network

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عنوان ژورنال:
  • Int. J. Web Service Res.

دوره 14  شماره 

صفحات  -

تاریخ انتشار 2017