POI recommendation method and recommendation system

A recommendation method and friend relationship technology, applied in the field of POI recommendation method and recommendation system based on spatio-temporal correlation factors, can solve problems such as unreliable data quality, low recommendation accuracy, and difficulty in determining spatio-temporal characteristics

A recommendation method and friend relationship technology, applied in the field of POI recommendation method and recommendation system based on spatio-temporal correlation factors, can solve problems such as unreliable data quality, low recommendation accuracy, and difficulty in determining spatio-temporal characteristics

CN110119475AActive Publication Date: 2019-08-13成都集致生活科技有限公司
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  • POI recommendation method and recommendation system
  • POI recommendation method and recommendation system
  • POI recommendation method and recommendation system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0094] In this example, if figure 1 As shown, a POI recommendation method, the method includes the following steps:

[0095]Step 101, according to the user-POI relational network that constructs from the user of LBSN portal website and POI data construction, utilize the embedding method learning of network to obtain the embedding vector of user and POI;

[0096] The user and POI data collected from the LBSN portal website include: user basic information, POI basic information, friendship between users, user check-in records and user comments, and the user check-in records and user comments include text content, time and location, the user basic information includes the user ID and user name, and the POI basic information includes the POI ID, POI name, and latitude and longitude.

[0097] The described utilizing network embedding method to learn and obtain the embedding vector of user and POI comprises steps:

[0098] The user-POI relationship network is divided into three su...

Embodiment 2

[0198] This embodiment is a POI recommendation system, including:

[0199] A network embedding module for converting the collected data of users and POIs into embedding vectors of users and POIs;

[0200] The dynamic factor module is used to establish a dynamic factor model according to the embedding vectors of users and POIs, and learn to obtain parameter values, and then solve to obtain the node value that maximizes the joint probability distribution of nodes;

[0201] The recommendation module is used to recommend POIs according to the product size of the edge probability and propensity corresponding to the maximum joint probability distribution of the factor graph nodes.

[0202] Based on the data and steps in Embodiment 1, the recommendation system of this embodiment is used to recommend POIs for users in the same manner.

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Abstract

The invention discloses a POI recommendation method and recommendation system, and relates to the technical field of recommendation, and the method comprises the steps: according to a user-POI relation network established by user and POI data collected from an LBSN portal website, using a network embedding method to obtain embedding vectors of the user and the POI; constructing a dynamic factor graph model comprising a plurality of correlation factors related to the user and the POI according to the embedding vectors of the user and the POI, establishing joint distribution on a space-time social network according to the plurality of correlation factors related to the user and the POI, and obtaining parameter values in the joint distribution through learning; calculating to obtain a node value enabling the joint distribution probability to be maximum, then calculating the edge probability of each node, and carrying out POI recommendation according to the product of the edge probabilityand the tendency. The problem of low recommendation accuracy caused by unreliable data quality and difficulty in determining the space-time mode is solved, and the recommendation performance is remarkably improved.

Description

technical field [0001] The present invention relates to the technical field of recommendation, in particular to a POI recommendation method and recommendation system based on spatio-temporal correlation factors. Background technique [0002] The emergence and popularization of mobile Internet technology has completely changed people's daily life and produced many new life service models, such as location-based social networking (LBSN) services. The massive data generated by LBSN contains information on user behavior and preferences, which can support location-based personalized services, such as point-of-interest recommendations. [0003] The existing technology has been improved to solve the problems of data sparsity and cold start, and has achieved ideal results, but it still faces two main problems: 1) the data quality is unreliable, and it is difficult to accurately obtain the correlation between data; 2) user interests are affected by The influence of multiple factors ...

Claims

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Application Information

Patent Timeline
13 Aug 2019
Publication
CN110119475A
IPC
G06F16/9535; G06F16/9537
Inventors
熊熙; 李元媛