Customized recommendation method based on graphs

A recommendation method and algorithm technology, applied in the field of recommendation, can solve the problems of sparse scoring data, short path length, and less overlap between two user selections.

Active Publication Date: 2016-08-31
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF4 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] (3) Running speed problem
[0012] 2) The length of the path connecting two vertices is relatively short;
[0017] There are mainly sparsity problems, cold start problems, scalability problems, etc. in the current recommendation system. The sparse problem is due to the large scale of data, less

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
  • Customized recommendation method based on graphs
  • Customized recommendation method based on graphs
  • Customized recommendation method based on graphs

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] The present invention will be further introduced below.

[0027] (1) Implied semantic analysis

[0028] The present invention adopts a Latent Semantic Model (LFM), and the main idea is to use the product of two low-dimensional matrices to represent the user's rating matrix for items. First, you need to collect the user's historical scoring records for items, and then use LFM to model them, and you can get the model shown in the following figure:

[0029]

[0030] The R matrix is ​​a user*item matrix, and the matrix value Rij represents useri's interest in itemj, which is exactly the required value. The LFM algorithm can extract several categories from the user's rating records of items, as a bridge between the user and the item, and the R matrix is ​​expressed as the multiplication of the P matrix and the Q matrix.

[0031] R U I = P U Q I = X k = 1 K P U , k Q k , I

[0032] The P matrix is ​​the user-class m...

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 provides a customized recommendation method based on graphs and can effectively reduce influence of sparsity on the recommendation effect. According to the method, a first step, a hidden meaning model is utilized to calculate historical scoring records of users to acquire hidden relationships among users and among objects; a second step, similarity among the users is calculated by utilizing the hidden relationships acquired in the first step, similarity among the objects is calculated, and a user graph and an object graph are constructed for the similar users and the similar objects; a third step, a user-object graph model is constructed by utilizing a user graph model and an object graph model acquired in the second step and a bipartite graph of the users and the objects acquired through utilizing the historical scoring records of the users; and a fourth step, the access probability of objects without scoring record of each user is ordered in a descending mode by utilizing a random walk personalrank algorithm, and front N objects are acquired to form a recommendation list for recommendation to the users.

Description

Technical field [0001] The invention belongs to the technical field of recommendation methods, and relates to a graph-based personalized recommendation method. Background technique [0002] Recommender System (RS) is a system that can actively recommend products or items to users by using users’ preferences. It uses users’ historical data to discover user preferences and push items that may be of interest to specific users. , A good recommendation system can bring considerable economic benefits to businesses. [0003] The composition of a complete recommendation system must include three elements: user model, recommended object model, and recommendation algorithm. The recommendation algorithm is the core of the recommendation system. At present, the more mature recommendation algorithms mainly include: recommendation based on collaborative filtering, implicit semantic model, recommendation based on graph model, combined recommendation, etc. [0004] An invention similar to the pre...

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
IPC IPC(8): G06Q30/06
CPCG06Q30/0631
Inventor 胡晶晶刘琳竹薛静锋单纯段智伟
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap