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

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...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a recommendation method for mining deep user similarity based on interaction sequence data, so as to solve the shortcomings of using the personalized recommendation system in the prior art

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