User space-time similarity measuring method in interest point prediction and recommendation

Abstract

The invention relates to the field of position prediction and recommendation, in particular to a user space-time similarity measuring method in interest point prediction and recommendation. The user space-time similarity measuring method in interest point prediction and recommendation includes the steps of preprocessing data; measuring space-time cooccurrence mode similarity; measuring user space-time similarity. The invention provides the user space-time similarity measuring method, the space-time cooccurrence mode and similarity thereof are defined through check-in time close degree and interest point position close degree, then the user space-time cooccurrence mode and similarity thereof are defined, user space-time similarity among users is defined based on the weight of interest point check-in frequency through the most similar space-time cooccurrence mode and similarity thereof, and the user space-time similarity measuring method is provided for a user-based collaborative filtering method in interest point prediction and recommendation.

Description

technical field [0001] The invention relates to the field of position prediction and recommendation, in particular to a user spatiotemporal similarity measurement method in point of interest prediction and recommendation. Background technique [0002] With the development of mobile terminals, wireless communication, mobile Internet and other technologies, users can record their whereabouts anytime and anywhere, and upload them to location-based social networks (such as Foursquare, Gowalla), and the user check-in data in social networks has expanded rapidly. The check-in data contains the information "who (user) visited and where (point of interest) when (time)", by analyzing the check-in data, it is possible to discover the user's behavioral characteristics and activity rules, reveal the user's interest preferences, and then serve the user Provide location-based services (such as forecasting and recommending points of interest and routes), assist service providers and users ...

AI Technical Summary

Problems solved by technology

However, the above-mentioned collaborative filtering method based on POI / user does not integrate the time factor of check-in, the geographical influence of POI, and the frequency / popularity of POI check-in into the user similarity measurement method, but mainly in the recommendation results. Fusion using a linear approach

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