Friends Recommendation Based on KBERT-CNN Text Classification Model

Abstract

In social networking platforms, the text published by users is usually the most direct way to express users' interests. This paper studies the method of text classification to mine user's interests for friends recommendation. And by fine-tuning the BERT (Bidirectional Encoder Representation from Transformers) pre-training language model to complete text classification tasks. Aiming at the problem of local information missing in the output of BERT pre-training language model. This paper proposes the KBERT-CNN (K_layers Bidirectional Encoder Representation from Transformers and Text Convolutional Neural Networks) text classification model. The model uses the output of the last four layers Transformers of the BERT pre-training language model as text vector, and combines with TextCNN(Text Convolution Neural Network) to build a text classification model. Then this paper uses the probability distribution of user's texts categories to calculate the interest similarity between users to achieve Top-N friends recommendation. Experimental results show that the F1 of the KBERT-CNN text classification model reaches 92.26%, which is better than other text classification models. The Precision of friends recommendation based on text classification is ahead of other content-based friends recommendation methods.