Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Tensor decomposition and weighted HITS-based time perception personalized POI recommendation method

A technology of tensor decomposition and recommendation method, which is applied in the field of time-aware personalized POI recommendation based on tensor decomposition and weighted HITS, which can solve the problems that the recommendation effect is not ideal and cannot provide personalized recommendation services.

Active Publication Date: 2017-07-18
ZHEJIANG HONGCHENG COMP SYST
View PDF6 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The obvious defect of the global recommendation method is that it cannot provide personalized recommendation services, while the method that can provide personalized POI recommendations depends on the scale of the user's location history. When the user's location history scale is small, the recommendation effect is often not ideal.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Tensor decomposition and weighted HITS-based time perception personalized POI recommendation method
  • Tensor decomposition and weighted HITS-based time perception personalized POI recommendation method
  • Tensor decomposition and weighted HITS-based time perception personalized POI recommendation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0054] Example: such as figure 1 As shown, a time-aware personalized POI recommendation method based on tensor decomposition and weighted HITS includes the following steps:

[0055] (1) User preference modeling based on collaborative tensor decomposition

[0056] Step 1: Enter user check-in historical data and POI category data, and construct a three-dimensional user preference tensor according to the frequency of user visits to a certain type of POI within a certain period of time (user, time period, POI category), and normalize it;

[0057] user preference tensor Modeling time-aware user preferences, constructing results such as figure 2 shown. POI categories represent POI functions and have different granularities, often expressed as a category hierarchy.

[0058] The present invention assumes that the POI category hierarchy already exists, and is divided into two layers, the first layer has n major categories, and the second layer has m small categories (n1 , u 2 ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to a tensor decomposition and weighted HITS-based time perception personalized POI recommendation method. For the problem of data sparsity confronted in a conventional POI recommendation method, firstly a user preference is modeled by introducing cooperative tensor decomposition of additional information; secondly POIs are scored by integrating the user preference and the popularity of the POIs through weighted HITS; and finally a plurality of POIs ranked in front are provided for a user according to the scoring of the POIs to serve as recommended POIs. By integrating the cooperative tensor decomposition and the weighted HITS and considering three factors including the user preference, time and local characteristics, the problem of the data sparsity is solved and high-quality personalized POI recommendation is provided for the user.

Description

technical field [0001] The invention relates to the field of POI recommendation, in particular to a time-aware personalized POI recommendation method based on tensor decomposition and weighted HITS. 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] There are two main types of traditional personalized POI recommendation methods: the first type is based on collaborative filtering (collaborative filter...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/9537
Inventor 王敬昌吴勇陈岭应鸳凯郑羽
Owner ZHEJIANG HONGCHENG COMP SYST
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com