Multidimensional cloud and user dynamic interest-based neural network recommendation method

Abstract

The invention relates to the field of information retrieval and data mining, and in particular to a multidimensional cloud and user dynamic interest-based neural network recommendation method. The method comprises the following steps of: obtaining a score, for a project, of a user and a scoring time of the user, and carrying out preprocessing; constructing a user-based multidimensional cloud modeland a project-based multidimensional cloud model by using the preprocessed data, so as to obtain a user-based predicted score and a project-based predicted score; and taking the user-based predictedscore and the project-based predicted score as input data of a trained neural network score prediction model so as to obtain a final prediction score. According to the method, user data is effectivelyutilized, and disadvantages of personalized recommendation methods under data sparse scenes are improved.

Description

technical field [0001] The invention relates to the fields of information retrieval and data mining, in particular to a neural network recommendation method based on multi-dimensional cloud and user dynamic interests. Background technique [0002] The continuous development of Internet and Web 2.0 technologies has generated a large amount of information data for users, which meets the needs of users for information in the information age, but information overload has a serious impact on information consumers and information producers, and it is the most effective solution to information overload. The method is the recommendation system. [0003] As an intelligent information service method, recommender systems are widely used by e-commerce, social networks, movies and videos, music, personalized emails, and advertisements. The recommendation system is represented by the electronic service personalization technology. It analyzes and calculates the historical behavior data (p...

AI Technical Summary

Problems solved by technology

[0004] Although the recommendation technology based on collaborative filtering has a good recommendation effect, in recent years we have observed that the availability of digital information, resources and online content has exploded, and users' evaluation information on items is missing a lot, resulting in data sparsity. Therefore, the traditional collaborative filtering recommendation method is no longer suitable for the current scale of the network

And due to the individual differences of users, the randomness and fuzziness of user evaluation information are caused. The randomness of user rating information means that users change their rating behavior under specific rating behaviors. The fuzziness of user rating information means that users can only use 1-5 The evaluation of the item is described in terms of points, which is vague and inaccurate. It is not more accurate to express the concept of qualitative language, which makes it difficult to predict the score with low accuracy.

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