Shida Feng
ABSTRACT
With sparse historical user-item interactions and redundant knowledge associations within the item knowledge graph for anonymous knowledge graph recommendation, it is challenging to utilize historical interactions and knowledge associations in a balanced way. Existing research defines the multi-order neighbors of users and aligns users to entities, which has the problems of high computational complexity, high user embeddings dependence on entity embeddings, and difficulty in migration. In this paper, we propose a collaboratively enhanced anonymized knowledge graph recommendation model (CE-KGR) to mine first-order similar users from historical user-item interactions, which has the advantages of low computational complexity and can flexibly migrate to the embedding-based knowledge graph recommendation models. Aiming to decouple user and entity embeddings, we use graph neural networks to explicitly aggregate similar users' information for updating user embeddings and mine similar users' potentially similar consumption preferences. We conducted extensive experiments on multiple public datasets. Compared to state-of-the-art recommendation models, CE-KGR obtains AUC gains of 0.63% and 2.92% and F1 gains of 0.59% and 2.78% on MovieLen1M and BookCrossing, respectively.
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Index Terms
- CE-KGR: Collaboratively Enhanced Knowledge Graph Recommendation
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