Towards personalized maps Article Swipe

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Computer science Recommender system Information retrieval Point (geometry) World Wide Web Point of interest User modeling Data science Data mining User interface Artificial intelligence Mathematics Geometry Operating system

Kaiqi Zhao , Yiding Liu , Quan Yuan , Lisi Chen , Zhida Chen , Gao Cong ·

YOU? · · 2016 · Open Access · · DOI: https://doi.org/10.14778/3007263.3007305 · OA: W2548961596

Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top of the user pre ference models for min ing and s e a r ch geo-textual data (called PreMiner) to support personalized maps. Different from existing recommender systems and data analysis systems, PreMiner highly personalizes user experience on maps and supports several applications, including user mobility & interests mining, opinion mining in regions, user recommendation, point-of-interest recommendation, and querying and subscribing on geo-textual data.