Content-based cross-domain recommendation method

A recommendation method and cross-domain technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve problems such as data sparseness and cold start
CN110232153AInactive Publication Date: 2019-09-13SOUTH CHINA UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
SOUTH CHINA UNIV OF TECH
Publication Date
2019-09-13
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention provides a content-based cross-domain recommendation method. The content-based cross-domain recommendation method comprises the following steps: S1, obtaining a user interest word list;S2, taking the text information of projects in a user behavior sequence of a source domain as source domain training data, and taking the text information of each project in the target domain as target domain training data; S3, constructing a content semantic coding network model; S4, using the content semantic coding network model trained in the step S3 to carry out content semantic coding on theuser behavior of the source domain and the project of the target domain to obtain a user behavior interest vector and a project semantic vector; and S5, for each user, calculating the similarity by using the interest vector and the project semantic vector of the user, and obtaining k most similar projects as recommended projects. Due to the content-based cross-domain recommendation method, the user interest vectors can be coded based on the text information of the projects in the source domain user behavior sequence and are matched with the projects in the target domain, so that cross-domainrecommendation is realized.
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Description

technical field

[0001] The present invention relates to the technical field of recommendation, and more specifically, to a content-based cross-domain recommendation method. Background technique

[0002] With the continuous development of mobile Internet technology, the amount of information in the network is rapidly expanding and increasing exponentially, and the problems of information overload and information wandering on the network are becoming more and more serious. In order to provide users with satisfactory information and services, recommender systems emerged as the times require, and have become a research field that many researchers pay attention to. The recommendation system performs information filtering by predicting the user's preference for information resources.

[0003] At present, the commonly used recommendation method is the recommendation algorithm based on collaborative filtering. This type of method is mainly used for single-domain recommendation, req...

Claims

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