Two-layer knowledge graph transformer network-based question and answer explainable recommendation

The question and answer (Q&A) recommendation in community question answering (CQA) helps users quickly and accurately find the desired Q&A. However, existing studies face the problems of sparse interaction data, cold starts, and a lack of explanations. This paper proposes a novel Q&A explainable recommendation approach based on a two-layer knowledge graph transformer network. It alleviates the sparse data and cold start problem by the novel two-layer knowledge graph. First, a two-layer knowledge graph in CQA is constructed. The interaction layer helps to enrich the associations between users and questions and answers (Q&As). The semantic layer provides semantic associations and reflects contextual domain knowledge. Second, a critical meta-path recognition module is constructed to learn the critical meta-paths between users and documents from the interaction layer. Then, a user and Q&A embedding method based on a two-layer knowledge graph is proposed to enhance the user and Q&A representations. Finally, a recommendation and explanation layer is established to obtain personalized Q&A recommendation results and corresponding explanations. Compared with the baselines, the proposed method shows superior performance. It achieves average improvements of 21. 28%, 28. 41% and 27. 18% in precision, recall and F1-measure, respectively, in the top- K Q&A recommendation separately. It improves the area under the curve and F1-measure of the click-through rate prediction recommendation by 11. 32% and 23. 06%, respectively.

Authors

• Ying Li
• Ming Li
• Jin Ding
• Yixue Bai

Keywords

• Question and answer recommendation
• Knowledge graph-based recommendation
• Two-layer knowledge graph
• Explainable recommendation