User preferences prediction approach based on embedded deep summaries

Some existing preference prediction methods have utilized users’ review texts to learn additional knowledge to support the prediction task. Such methods determine and represent users’ preference knowledge by conducting user sentiment, aspect sentiment and topic analysis as recognized in the review texts. However, the discovered item topics from topic-based methods may not fit the preferences of most users while the discovered users’ opinions and aspects’ sentiments from sentiment based methods may not reflect each user's opinion. This paper proposes a hybrid approach to learn and represent users’ preference knowledge from review texts and utilize the acquired representation to support rating prediction. Our approach assumes that user preferences are affected by relevant item aspects and majority preference which can be captured through proper summarization and representation of users’ review texts. Thus, two deep learning practices are established: the recurrent neural network - Long Short-Term Memory (RNN-LSTM)architecture to learn users’ preference knowledge along with item aspects which influence preferences and the Doc2Vec algorithm to convert the acquired knowledge to a suitable representation. The approach extends probabilistic matrix factorization (PMF)model by strengthening its latent factors predictions with the acquired preference knowledge which is used to regulate the predictions. Our experiments on the Amazon products dataset have revealed the capability of learning a suitable representation for users’ preference knowledge and its impact on rating prediction as our proposed approach beats alternative methods.