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

User self-similarity-based multi-model combination movie recommendation method

A self-similar, multi-model technology, applied in structured data retrieval, electronic digital data processing, character and pattern recognition, etc.

Inactive Publication Date: 2017-11-21
BEIJING INSTITUTE OF TECHNOLOGYGY
View PDF9 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the current recommendation algorithm for movies, there is no recommendation method that considers user self-similarity

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
  • User self-similarity-based multi-model combination movie recommendation method
  • User self-similarity-based multi-model combination movie recommendation method
  • User self-similarity-based multi-model combination movie recommendation method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0097] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0098] Using the multi-model movie recommendation method based on user self-similarity of the present invention to recommend movies for users, the operation process is as follows figure 1 As shown, the specific operation is:

[0099] Step 1. Build the database.

[0100] Obtain movie review information, extract user ID, movie ID and corresponding rating information in the movie review information from the movie review information, and construct a movie rating database; extract movie information from the movie review information to construct a movie content database.

[0101] The movie information includes a movie ID, a movie name, and a movie type.

[0102] In this embodiment, the movie review information is the Movielens 100k offline data set downloaded from the GroupLens official website as a database. The latest dataset...

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 user self-similarity-based multi-model combination movie recommendation method and belongs to the technical field of data science and data mining. According to the method, results obtained by collaborative filtering and content filtering are fused dynamically based on the self-similarity of score information of users for movies to obtain a recommendation result. When multi-value attributes of the movies are combined with classification tree models in machine learning, it is proposed that attribute values of a movie are separated to form independent eigenvectors and the multi-value attributes are well combined with the classification tree models. Compared with an existing method, the user self-similarity-based multi-model combination movie recommendation method has the advantages that the recommendation result obtained by fusing the results obtained by collaborative filtering and content filtering dynamically based on the self-similarity of the score information of the users for the movies better meets the user demand, and the recommendation quality is higher.

Description

technical field [0001] The invention relates to a movie recommendation method based on user self-similarity and multi-model combination, which belongs to the technical field of data science and data mining. Background technique [0002] "Information overload" makes it difficult for users to find helpful information from the vast ocean of information, so users need information screening. Machine learning enables machines to gradually undertake some repetitive tasks, and may also discover information that is difficult for humans to see. Therefore, various recommendation engines have played a better role. The existing recommendation algorithms are mainly divided into a single recommendation model and a fusion method of multiple recommendation models. Since the fusion of multiple recommendation models can play a complementary role, the recommendation accuracy is higher. When combining user-based collaborative filtering with content-based collaborative filtering, Zhao Chenting...

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): G06F17/30G06K9/62
CPCG06F16/21G06F18/25G06F18/214
Inventor 张欣林灵吕坤
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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