Interest point group recommendation method based on geographical locations

Abstract

The invention belongs to the field of service recommendation based on geographical locations. The method is characterized in that multiple type points can be returned through a one-time request according to user requirements to form combined recommendation. The method sequentially includes the following steps that 1, a scanning line is used for conducting scanning from right to left, and visible points of all the points are found and connected in the scanning process to form a visible net of the whole network; 2, by means of a recursive concept, a 2-isomerous point group is constructed from the simplest single-point structure, and in this way, (N-1)-isomerous point groups can be obtained sequentially. In order to reduce unnecessary matching, the L layer near point concept is provided, and namely only some point groups close to the current point are taken into consideration during matching; GN-1 is obtained in the step and comprises the (N-1)-isomerous point groups; 3, based on the previous bedding, alternative N-isomerous point groups are formed in the step, the first K optimal combinations are kept always through comparison sorting to obtain a final set B including the K optimal combinations, and the set B is returned to a user.

Description

technical field [0001] Location-based point-of-interest recommendation service domain. Background technique [0002] Location-based services (LBSs) are becoming an increasingly important part of our lives, and all existing LBS providers support finding nearby points of interest. In this typical LBS, the user submits the current location and interested points of interest to the server, and the server returns to the user some nearby points of interest that meet the user's requirements for reference and selection by the user. But in real life, people usually don't only visit one point of interest, but go to many different places in succession. For example, a person wants to go to the movies after dinner. In this case, the user hopes to get a point of interest. (a combination of a restaurant and a movie theater), it is required that these two points have a high evaluation respectively, and at the same time they are geographically adjacent to facilitate transfer from one place t...

AI Technical Summary

Problems solved by technology

Providing "N-in-One" services is challenging, and "N-in-One" services are not equivalent to N times a single type of service, because the location correlation of N points and the resulting time and space complexity must be considered degree problem

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