Item-interaction constraint-based autoencoder model for recommendation
In recent years,autoencoders have been widely used in the recommendation domain due to their strong data compression capabilities.However,research has shown that due to the sparsity of data in recommendation systems,autoen-coder models can develop biases during training caused by the lack of interactions between users and items,negatively im-pacting recommendation results.To address this issue,we propose a recommendation model based on item interaction con-straints.The model uses item interaction as a constraint and designs new parameter update rules to avoid biases introduced by data sparsity in model training.Additionally,the model incorporates item tag information into the training process,lever-aging new data sources to mitigate the effects of data sparsity and improve recommendation performance.Experiments on three datasets with varying sizes and sparsity levels demonstrate that the model is well-suited for sparse datasets,effectively enhancing recommendation accuracy and showing great application potential.
万方数据
维普
