Cognitive-based knowledge learning framework for recommendation

Xue Chen, Qichao Liang, Yuanzhu Chen, Peng Wang, Hang Yu, Xiangfeng Luo

Abstract: Highlights•We highlight the importance of cognitive psychology to improve click-through-rate (CTR) in recommender systems (RS) and develop a Cognitive-based Knowledge Learning Framework (CKLF) for recommendation. To our best knowledge, CKLF is the first framework which applies cognitive psychology into KG-based RS.•We propose Spreading Activation Network (SAN) and Sequence-sensitive Attention Mechanism (SAM) in CKLF. SAN employs spreading activation theory to achieve the high-order connectivity on the Knowledge Graph (KG). SAM employs the Ebbinghaus forgetting curve to track interest evolution of users. Both mechanisms make it possible to obtain diverse information associated with interactions.•We conduct extensive experiments using real-world datasets and KGs, which demonstrates the superiority of CKLF compared to state-of-art baselines and its enhanced performance in diversity and serendipity. We performed ablation and sparsity experiments to demonstrate the effectiveness of both SAN and SAM. In addition, we designed a case study to provide deeper insight on the relation between accuracy and beyond-accuracy.