Multi-mode deep auto-encoder recommendation model for fusion of text information

Abstract

A new recommendation model is proposed to solve the problems of data sparsity and deep semantic information learning when using score information as auxiliary recommendation. Feature learning of scoring information is achieved through depth auto-encoder to encode and decode implicit feedback scoring matrix. And the features of type text information are learned through the Flatten Layer and Full-connection Layer operate, with user-film type matrix being the input to the Embedded Layer. Meanwhile, the model uses BERT+BiLSTM structure to realize features learning and features splicing of contextual information on film title text. After the fusion of the three features, the prediction score is obtained by auto-encoder processing. Movielens 1 M and Movielens 100 k are used as datasets, mean absolute error and mean square error are used as evaluation indexes, and SVD, PMF, PMMMF, SCC, RMbDn and Hern are used as comparison models. The results show that the MAE values of the model are reduced to 0.045 8 and 0.046 0 respectively, and the MSE values of the model are reduced to 0.027 3 and 0.039 0 respectively, which are better than the results of the comparison algorithms. The new recommendation model achieves performance improvement.

Keywords

• recommendation algorithm
• bert
• bilstm
• deep auto-encoder
• textual information