BKGNN-TI: A Bilinear Knowledge-Aware Graph Neural Network Fusing Text Information for Recommendation

Abstract

Knowledge graph (KG)-based recommendation methods effectively alleviate the data sparsity and cold-start problems in collaborative filtering. Among these methods, neighborhood-based methods are the mainstream methods. However, these methods ignore some meta-information about the items, specifically, the diversity of item information (e.g., texts) and feature interaction between neighboring nodes. In this paper, we propose a Bilinear Knowledge-aware Graph Neural Network Fusing Text Information (BKGNN-TI), which can model both knowledge graph information and text information. In particular, the information in knowledge graph contains not only the existing high-order connectivity but also feature interactions between neighboring nodes at the same level in KG. First, we construct the information propagation layer using the bilinear collector and linear collector. Feature interactions between neighboring nodes and the high-order connectivity are collected in the information propagation layer to generate the item knowledge representations. The bilinear collector emphasizes the importance of second-order feature interaction between neighboring nodes in the KG. Then, texts are also introduced when computing the item representations, which can help further infer user interests. We choose objective program titles and introductions as text information to avoid the influence of subjective factors. BKGNN-TI designs an ALBERT-based sequence encoder to encode texts by the structure of ALBERT+Bi-LSTM+Attention, thus enriching the feature representations of the items. In the experiments, we utilize two language datasets, i.e., the English public dataset Movielens-20M and the Chinese dataset IPTV constructed by ourselves. The results demonstrate that our BKGNN-TI outperforms baselines, indicating that our BKGNN-TI is a generalization for both Chinese and English datasets.