Professional field system cold start recommendation method based on knowledge graph

A professional field and knowledge graph technology, applied in the fields of natural language processing and data mining, can solve problems such as inability to achieve system cold start, poor accuracy, etc.
CN110427563AActive Publication Date: 2019-11-08杭州智策略科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
杭州智策略科技有限公司
Publication Date
2019-11-08

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Abstract

A professional field system cold start recommendation method based on a knowledge graph comprises the following steps of (1) constructing the professional field knowledge graph in a semi-automatic mode, firstly, the professional field knowledge graph is manually initialized based on a professional field classification directory, and then the professional field knowledge graph is automatically expanded through internet knowledge; (2) respectively extracting tags from registration information of a user and a description text of an article, and training a professional field word vector based on an internet text; (3) firstly, respectively carrying out entity linking on the user label and the article label in the knowledge graph, and then calculating a user / article matching degree value based on the shortest path between link nodes; and (4) recommending a plurality of articles with the highest matching degree values to the user. User registration information and article content informationare considered at the same time, and cold start of the system is achieved; a knowledge graph is constructed based on professional domain knowledge, and accurate and quantitative matching of user registration information and article content information is realized.
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Description

technical field

[0001] The present invention relates to natural language processing and data mining technology, in particular to a recommendation method. Background technique

[0002] The cold-start recommendation problem is divided into three categories: user cold-start recommendation, item cold-start recommendation and system cold-start recommendation. Among them, user cold start refers to making recommendations for new users, item cold start refers to recommending new items to interested users, and system cold start refers to personalized recommendations in a newly developed system (that is, no user behavior data at all). Obviously, the system cold start has the least available data and is the most difficult to implement.

[0003] The existing methods to solve the cold-start recommendation problem mainly include the following: (1) Provide non-personalized recommendations (such as recommended hot list), but the items recommended by this method cannot meet the personalized...

Claims

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