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Similarity Mining Method of User Behavior Based on Spatial-Temporal Patterns

A similarity and similarity distance technology, applied in special data processing applications, instruments, calculations, etc., can solve the problem that the time interval between check-in points cannot be too far apart, and achieve the effect of good similarity

Active Publication Date: 2018-12-21
XIAMEN YAXON NETWORKS CO LTD
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Problems solved by technology

There is also a method to use the POI attributes of check-in points to construct semantic themes, and judge user behavior similarity through topic similarity (patent 201310336664.0), but this kind of method also requires that the time interval between user check-in points should not be too far apart, otherwise, check-in every few months does not have any thematic meaning in itself

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  • Similarity Mining Method of User Behavior Based on Spatial-Temporal Patterns

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Embodiment Construction

[0033] The present invention will be further described in conjunction with specific embodiments.

[0034] As a specific embodiment, a spatio-temporal pattern-based user behavior similarity mining method of the present invention includes the following steps:

[0035] Step 1: Spatio-temporal data fusion step, obtain all check-in data of a single user, the check-in data includes check-in time, check-in point location information, arrange the check-in points in order of check-in time, and divide the check-in points into isolated points and There are two types of rank subsets. The isolated point only retains the location information. The rank subset contains several check-in points, and the check-in time is converted into rank information. The rank subset retains the check-in point position and rank information.

[0036] Step 2: Spatio-temporal pattern matching step: using the spatio-temporal Hausdorff distance matching method, based on the similarity of user behavior calculated ba...

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Abstract

The invention relates to the technical field of user behavior trace mining, in particular to a user behavior similarity mining method based on the space-time mode. The space-time mode matching method of a sign-in point set is designed to carry out user behavior mining on data in consideration of the condition that sign-in records of LBSN users are discontinuous and seriously fragmented; sign-in points are classified into a sequence subset and isolated points, and time sequence information in the sequence subset is reserved and effectively integrated with position information in the similarity comparison process. By means of the new space-time Hausdorff distance matching method, space-time mode similarity discrimination can be effectively carried out, behavior trace recovery of the sign-in points is not needed, the sign-in time sequence information is effectively utilized, and therefore the similarity of user sign-in behaviors can be well judged.

Description

technical field [0001] The invention relates to the technical field of user behavior trajectory mining, in particular to a method for mining user behavior similarity based on spatio-temporal patterns. Background technique [0002] A large amount of check-in data is generated every day in LBSN (Location-Based Social Network). These data include information such as time, location, and POI attributes, reflecting the user's real life trajectory and interest tendencies. Mining these check-in data based on social networks, looking for people with similar behavioral interests can quantify and estimate the characteristics of people's social activities, and then discover people's behavior rules, so that people can have a deeper understanding of the life of communities in smart cities Trajectories, social behaviors, environmental changes, etc., can not only meet the increasingly strong personalization and socialization needs of LBSN users, provide support for the development of socia...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 涂岩恺黄家乾时宜陈典全
Owner XIAMEN YAXON NETWORKS CO LTD
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