Recommending method based on knowledge learning

A recommendation method and knowledge learning technology, applied in the field of user recommendation, can solve problems such as failure to improve recommendation effect and sparse scoring matrix, achieve good recommendation service experience, good recommendation results, and solve heterogeneity problems

Active Publication Date: 2019-03-22
CHENDU PINGUO TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The traditional method of coordinated filtering to establish a scoring matrix cannot take a suitable way to effectively integrate other behaviors with target rows and item informat

Method used

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  • Recommending method based on knowledge learning
  • Recommending method based on knowledge learning
  • Recommending method based on knowledge learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] In this example, if Figure 1~3 As shown, a recommendation method based on knowledge learning, the method includes the following steps:

[0061] S1. Extract users and items as entities, and extract user operation behaviors and item attributes as relationships to obtain user-item data;

[0062] S2. Convert the user-item data into triples to obtain triple data including entities and relationships;

[0063] S3. Store the triplet data in RDF, use transE to learn knowledge representation, convert entities and relationships into vector representations, and obtain user-item knowledge graphs; the relationships include relationships between users and items and relationship between items;

[0064] The knowledge map of this embodiment is as follows image 3 As shown, there are 3 users A, B and C, items 1, 2, 3, 4, 5 and 6, and user operation behaviors: browsing, adding to shopping cart and purchasing. This embodiment is only to illustrate the method. The set knowledge map is r...

Embodiment 2

[0092] A recommendation system comprising:

[0093] 1. Triple generation module: used to extract users and items as entities, and extract user operation behaviors and item attributes as relationships, obtain user-item data, convert the user-item data into triples, and obtain entities including And the triple group data of relationship; The triple group generation module includes:

[0094] 1.1. Entity extraction module: used to extract users and items as entities;

[0095] 1.2. Relationship extraction module: used to extract user operation behaviors and item attributes as relationships;

[0096] 2. Spectrum storage module: used to store the triplet data in a database;

[0097] 3. Knowledge representation learning module: used for knowledge representation learning, converting entities and relationships into vector representations, that is, obtaining user-item knowledge graphs; this embodiment is transE;

[0098] 4. Recommendation module: Associate the vectors of entities and ...

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Abstract

The invention discloses a recommending method based on knowledge learning, belonging to the technical field of user recommendation. The method overcomes the shortcomings of the prior collaborative filtering technology. The recommending method of the invention utilizes more interactive data between users and articles integrated by a knowledge map, and improves the recommending effect by embedding an improved sub-map. Knowledge map provides a new idea of fusion of heterogeneous data into collaborative filtering algorithm to solve the heterogeneity problem. The invention also provides a recommendation system used for a recommendation method, which is convenient for users to generate a recommendation list in time and quickly, obtains a better recommendation result, and provides a better recommendation service experience for users.

Description

technical field [0001] The invention relates to the technical field of user recommendation, in particular to a recommendation method and system based on knowledge learning. Background technique [0002] With the development of the Internet, various websites and apps have experienced information overload to varying degrees. How to choose suitable content from a lot of information to interested users is a problem faced by every website and app. The characteristics of the click message can be used to obtain the potential interest of the user, so as to push the message that the user is interested in to the user, thereby increasing the revenue of the website / App. [0003] Traditional personalized recommendation methods are based on user / item similarity, or based on collaborative filtering (matrix decomposition), and some hybrid methods. It may be considered that information is an item to be recommended. In the existing collaborative filtering technology, a scoring matrix of use...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/36
Inventor 王丹徐滢
Owner CHENDU PINGUO TECH
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