Implicit feedback recommendation method based on node2vec and deep neural network

Abstract

The invention discloses an implicit feedback recommendation method based on node2vec and a depth neural network, belonging to the technical field of data processing. The invention comprises the stepsof obtaining a user potential vector and an item potential vector; obtaining a user context prediction and a project context prediction; generating a user preference prediction for the project and training the user preference prediction; performing joint training. The method can well take into account the rich metadata information of users and items, and has high recommendation accuracy and modeltraining efficiency.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an implicit feedback recommendation method integrating node2vec and deep neural network. Background technique [0002] In the era of information explosion, recommender systems have played a huge role in alleviating information overload. Personalized recommendation systems are widely used in online service networks such as e-commerce and social media. Traditional recommendation models users' item preferences based on past interactions. Current solutions for recommendation using neural networks focus on explicit feedback and only model rating data. At the same time, when it comes to the interaction between users and item features, matrix decomposition is still used. The recent recommendation trend has shifted from explicit evaluation to implicit feedback, such as purchase, click, watch, etc. It cannot directly reflect user preferences, but the collection cos...

AI Technical Summary

Problems solved by technology

The sparsity level of the data directly leads to the limitations of traditional collaborative filtering methods, namely matrix factorization and its various extension methods

Images