Personalized POI recommendation method based on multi-influence embedment

A recommendation method, POI-technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as data sparsity and cold start problems

Active Publication Date: 2017-09-05
ZHEJIANG UNIV
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Problems solved by technology

[0009] In order to solve the data sparsity and cold start problems of current personalized POI recommendatio

Method used

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  • Personalized POI recommendation method based on multi-influence embedment
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  • Personalized POI recommendation method based on multi-influence embedment

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[0066] In order to describe the present invention in more detail, the technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0067] Such as figure 1 Shown is the flowchart of the personalized POI recommendation method based on multi-impact embedding of the present invention, according to figure 1 It can be seen that the personalized POI recommendation method based on multi-impact embedding of the present invention mainly includes three stages, namely: bipartite graph and check-in sequence construction, graph and sequence joint embedding learning, and POI scoring and recommendation.

[0068] The first stage: the construction of bipartite graph and check-in sequence

[0069] This stage is mainly to construct 7 bipartite graphs and check-in sequences. The 7 bipartite graphs are: user-user graph, user-gender graph, POI-category hierarchy graph, POI-regional hierarchy graph, user-time period gr...

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Abstract

The invention discloses a personalized POI recommendation method based on multi-influence embedment. By conducting associative embedded learning on seven bipartite graphs (a user-user graph, a user-time frame graph, a POI-time frame graph, a POI-region hierarchy graph, a POI-category hierarchy graph, a user-gender graph and a user-POI graph) and a check-in sequence, influences on the aspects of social contact, time, geography, semantics, user gender, user preference and sequence are integrated; meanwhile, certain expansibility is achieved, influences on other aspects can be conveniently integrated, so that the problems of data sparsity and cold start are effectively solved, and high-quality POI recommendation is provided for users.

Description

technical field [0001] The invention relates to the field of POI recommendation, in particular to a personalized POI recommendation method based on multi-influence embedding. Background technique [0002] With the rapid development of smart devices equipped with GPS, Location-based Social Networking Services (LBSNs), such as Foursquare, Facebook Places, GooglePlaces, etc., have emerged. On LBSNs, users can log in (check-in) POI (Point of Interest) such as stores and restaurants and share them. Due to the large number of users of LBSNs and the ability to cover a wide area, a POI recommendation service has appeared on the basis of LBSNs, which can not only help users recognize new POIs and explore unfamiliar areas, but also facilitate advertisers to push mobile advertisements to target users. [0003] However, inferring users' preferences for POIs through their location history is quite challenging. First, the POIs that a user can access are limited, and there are a huge num...

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Application Information

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IPC IPC(8): G06F17/30
Inventor 陈岭应鸳凯
Owner ZHEJIANG UNIV
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