Recommending method for mining depth user similarity based on interactive sequence data

A technology of user similarity and interaction sequence, which is applied in the recommendation field of mining deep user similarity based on interaction sequence data

Active Publication Date: 2019-03-26
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a recommendation method for mining deep user similarity based ...

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  • Recommending method for mining depth user similarity based on interactive sequence data
  • Recommending method for mining depth user similarity based on interactive sequence data
  • Recommending method for mining depth user similarity based on interactive sequence data

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Embodiment Construction

[0073] The recommendation method for mining deep user similarity based on interaction sequence data proposed by the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Advantages and features of the present invention will be apparent from the following description and claims. It should be noted that all the drawings are in a very simplified form and use imprecise scales, and are only used to facilitate and clearly assist the purpose of illustrating the embodiments of the present invention.

[0074] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the ...

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Abstract

The invention provides a recommending method for mining depth user similarity based on interactive sequence data. The recommending method comprises the following steps: pre-processing the original recorded data of the interaction between the user and the article to obtain interactive sequence data of the user and the article, and generating the user according to the interactive sequence data; themethod comprises the steps of: mining the depth user similarity based on the interactive sequence data; mining the depth user similarity; mining the depth user similarity based on the interactive sequence data. Item Interaction Matrix and Users-Gram matrix; In accordance with that user -Item interaction matrix and the user-gram matrix constructs a recommendation model and trains the recommendationmodel; Based on the trained recommendation model, each user's preference for all items is calculated, and all items are sorted from high to low according to the preference value. The items with the highest preference value among the remaining items are recommended to the corresponding users as the result of personalized recommendation by removing the items that the user has interacted with. The invention solves the problem of data sparseness and improves the recommendation accuracy by applying the preference of the user to the article and the similarity among the users to the recommendation method.

Description

technical field [0001] The invention relates to the technical field of the Internet, in particular to a recommendation method for mining deep user similarity based on interaction sequence data. Background technique [0002] With the development of the Internet, people are facing the problem of information explosion. On the one hand, a large amount of information can provide people with more choices, for example, people can choose their favorite movies. But on the other hand, too much information will cost people a lot of time to search and choose on the Internet. Therefore, the recommendation system is particularly important as a method to solve information overload, and the recommendation system has been widely adopted by many online services, such as online music, video, e-commerce, social networking, etc. The key to building a personalized recommendation system is to recommend a small number of items to each user according to the user's preferences. In the field of rec...

Claims

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Application Information

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IPC IPC(8): G06F16/9535G06F16/2458G06Q30/06
CPCG06Q30/0631
Inventor 徐亚南朱燕民沈艳艳俞嘉地
Owner SHANGHAI JIAO TONG UNIV
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