PStarπ: A Personalized Spatiotemporal Recommendation Systemor POI in Mobile Environment
Date Issued
2008
Date
2008
Author(s)
Sung, Pin-Chieh
Abstract
Recommendation systems for points of interest on location-based services have gotten widely popular as mobile devices and techniques progress in recent years. These systems usually use collaborative filtering approaches to achieve personalized recommendation and the spatial information is employed as an important factor inside to assist in improving recommendation quality. However, aside from users'' locations, time also highly affects where users plan to go or what users prefer to see on location-based services, and the temporal information can benefit recommendation results very well. In this thesis, we propose a novel approach to partition data by spatiotemporal clustering and to utilize collaborative filtering with much better scalability and accuracy. The characteristics of users'' preferences under different spatiotemporal situations will be revealed and preserved simultaneously under spatiotemporal data partitioning. Experiments show the recommendation quality provided by our PStarπ (Personalized Space-Time-Aware Recommender for Points of Interest) system is much more efficient, precise as well as satisfactory to users'' needs.
Subjects
location-based service
recommendation system
collaborative filtering
points of interest
data clustering
data partitioning
Type
thesis
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