文章基本信息

标题：An Novel Location Aware Spatial Data Recommender Using MobiFeeds
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作者：P.Alekya ; Bullarao Domathoti ; P.Nageswara Rao 等
期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2015
卷号：3
期号：7
DOI：10.15680/ijircce.2015. 0307022
出版社：S&S Publications
摘要：A location-aware news feed system enables mobile users to share geo-tagged user-generated messages,Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand,supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, nonspatialratings for spatial items, and spatial ratings for spatial items.We present a framework designed for schedulingnews feeds for mobile users. Mobi Feed consists of three key functions, location prediction, relevance measure, andnews feed scheduler. The location prediction function is designed to predict a mobile user’s locations based on anexisting path prediction algorithm User rating locations are exploited by user partitioning technique which influencesrecommendations with ratings close to the querying user spatially, without diminishing the system scalability andrecommendation quality. Item locations are exploited by using travel penalty technique which favors recommendationswhich are close in travel distance to the querying user by avoiding exhaustive access to all items.
关键词：Spatial Databases and GIS;LARS; Location; Scalable