Hybrid collaborative filtering movie recommendation model based on bilateral network structure

Abstract

The invention relates to a hybrid collaborative filtering movie recommendation model based on a bilateral network structure, and belongs to the field of recommendation systems. The model introduces anidea of deep learning and uses posters and description texts of movies, effective feature expression of users and articles is generated through CNN, a probability matrix decomposition technology is used to introduce potential users and movie models through Gaussian noise parameters, and a deep neural recommendation model with a bilateral network structure is integrated and built. The model is applied to open source dataset of ML_1M expressed by MovieLens_1M from a MovieLens website to perform experiments, so that it can be verified that when the deep learning is used to introduce auxiliary information of movie posters and description texts for building the hybrid collaborative filtering recommendation model, under the condition that the sparse interactive scoring matrix of the users and articles is improved, whether or not the influence of the data sparsity on recommendation accuracy is reduced. In the case of sparse data, the hybrid collaborative filtering movie recommendation modelbased on the bilateral network structure has the advantages that the ability to generate effective feature expression is more obvious, the influence of data sparsity is reduced, and the problem of inaccurate recommendation is effectively improved.

Description

technical field [0001] The invention belongs to the field of recommendation systems and relates to a hybrid collaborative filtering movie recommendation model based on a bilateral network structure. Background technique [0002] Traditional recommendation models only unilaterally consider the use of user rating data or item and user description information to generate recommendations, and the generated prediction results are often difficult to meet practical applications. However, as one of the important research directions in the field of recommendation system, the hybrid collaborative filtering recommendation algorithm can effectively avoid the sparsity of scoring data, the cold start of new items and the impact of scalability. By using deep learning methods to introduce auxiliary The information generates an effective implicit feature expression, and comprehensively considers the scoring data, item and user description information to build a hybrid recommendation model, t...

AI Technical Summary

Problems solved by technology

However, these recommendation models only unilaterally generate recommendations based on user description information, item attribute information, and rating data, and are easily affected by factors such as the sparsity of the rating matrix of users and items, cold start of new items, and scalability.

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