Collaborative knowledge-aware recommendation based on neighborhood negative sampling. Issue 115 (May 2023) - Lin, Zewei; Qu, Liping
Abstract: Knowledge graph and negative sampling, as the sources of auxiliary information, have been playing a vital role since they were included in the recommendation system. Creating an end-to-end model based on a graph neural network is the current technical trend. In order to generate samples with negative signals for the recommended model, negative sampling is also done from the unobserved data. However, the current end-to-end model based on graph neural network is unable to effectively capture the high-order collaboration signals of items, making it unable to learn the high-quality user and item representation. Additionally, existing negative sampling technique is insufficient for producing high-quality negative samples that can both reflect users' true preferences and offer information for model training. Therefore, this paper proposes a new model, namely, collaborative knowledge-aware network based on neighborhood negative sampling (CKNNS). Users and items are technically modeled based on the collaboration-aware and knowledge-aware. To help the model better capture the users' preferences and the personality traits, a gated aggregation strategy is used to adaptively capture collaboration-aware signals and knowledge-aware information. Meanwhile, a negative sampling method based on user's neighborhood has been proposed. Specifically, the similarity of users is judged according to their collaboration information, so as to build a negative sampling domain for a specific user and improve the quality of negative sampling. The experimental outcomes on four benchmark datasets reveal that CKNNS outperforms sophisticated approaches like LightGCN, CKAN, KGIN and KGCL.