Cognitive-based knowledge learning framework for recommendation.

• 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. Recommender systems (RS) have been widely used in Web applications such as search engines, social media platforms and e-commerce portals. Accuracy-related metrics have always been the primary goals for RS. Most RS tend to recommend items that are most relevant or similar to users' historical records, which may narrow users' cognitive scope and lead to information cocoons. This paper proposes a Cognitive-based Knowledge Learning Framework (CKLF), which addresses this challenge by integrating cognitive psychology to the construction of the neural network. CKLF consists of two key parts: a Spreading Activation Network (SAN) and a Sequence-sensitive Attention Mechanism (SAM). The SAN uses spreading activation theory to activate diversified and high-ordered entities in an auxiliary knowledge graph, and to refine embeddings of users and items. The SAM incorporates the Ebbinghaus forgetting theory into the attention mechanism to capture evolution features of users' sequential interactions. Extensive experiments on two public benchmarks show, compared with start-of-the-art baselines, CKLF not only improves accuracy but more importantly achieves superior performance in the beyond-accuracy objectives such as diversity and serendipity. Therefore, CKLF can effectively alleviate information cocoons and improve the overall quality of RS. [ABSTRACT FROM AUTHOR]