Information overload makes a big obstacle for multimedia services. To alleviate the burden, collaborative filtering has been actively studied in the recommendation field to help users find satisfactory content. However, current methods fail to comprehensively predict users’ interactions since users’ interests are complex and multifaceted. We argue that the prevalent graph-based models pay attention to shared characters among neighboring node users or items while ignoring their specific characters. In this work, we investigate common and special characters hidden in users’ interactions with the help of semantic knowledge to model users’ interests. We propose a dual-channel common and special embedding-based collaborative filtering model (CSE) for personalized recommendation. CSE adopts a divide-and-conquer strategy to capture commonality and semantic speciality with graph convolutional learning and knowledge translation. Ultimately, common and special embeddings of users’ interests participate in pairwise user-item matching to score their interaction for personalized recommendation. Extensive experiments on two real-world datasets demonstrate the rationality of the proposed CSE. It verifies that the proposed CSE can comprehensively model user interests with common and special characters and improve recommendation performance.
Common and Special Embedding-Based Collaborative Filtering
Lect. Notes Electrical Eng.
International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021
Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021) ; Chapter : 110 ; 1107-1116
2022-03-18
10 pages
Article/Chapter (Book)
Electronic Resource
English
Common and Special Embedding-Based Collaborative Filtering
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