Personalized Product Recommendation Model of Automatic Question Answering Robot Based on Deep Learning

Abstract

The collaborative filtering algorithm widely used in recommendation systems has problems with the sparsity of scoring data and the cold start of new products. A personalized product recommendation model for automated question-answering robots using deep learning is proposed. First, a personalized attention mechanism at the word level and the comment level is proposed, and the comments and users are individually coded. Then, the bidirectional gated recurrent unit (Bi-GRU) is used to construct the score prediction matrix, and through the dynamic collaborative filtering algorithm to integrate the time characteristics of the user’s interest changes. Finally, the feature codes of the users and products are input into the Bi-GRU model for learning, so as to output the recommendation list of personalized products of the automated question answering robot. Experimental results based on the JD and Tianchi datasets show that the training loss of the proposed model is lower than 45 and 23, respectively. And HR@15 and MRR@15 exceed 48 and 15, respectively, which are better than other comparison models. It can better adapt to the actual needs of automatic question-answering robots.