Backward path growth for efficient mobile sequential recommandation
The sequential recommendation from data mining has high computational risks. For avoiding that key challenges we provide the mobile based recommendation system for optimal cost based shortest path method. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Traffic issue has become one of the most concerns for citizens in metropolis. It is extremely hard to find an available vehicle in hot region during the peak hour. Due to the supply demand contradiction, the authority is still seeking effective and economical carpooling solutions for “Taxi taking dilemma” without adding more vehicles. To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code (QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a Smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications.
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