MSKD: A Knowledge Denoising Framework for Metaverse Service Recommendation

Hongliang Sun, Jinlan Liu, Can Wang, Jiabao Kang, Zhiying Tu, Dianhui Chu, Xiaofei Xu

Abstract: The diversified and personalized service matching provided by the metaverse is hindered by challenges such as sparse interaction data, the long-tail effect, and the fragmentation of user demand profiles. Knowledge graph (KG)-based service recommendation methods alleviate these issues by incorporating external knowledge related to service items. However, their effectiveness is often compromised by irrelevant KG entities and relations. This irrelevant knowledge noise leads to two core challenges: (1) incomplete encoding of service knowledge into user and item embeddings, and (2) the absence of reliable true noise labels for distinguishing low-relevance knowledge information. To overcome these challenges, we propose MSKD, a metaverse service recommendation framework based on knowledge denoising. MSKD first constructs a user preference KG by fusing service-item external knowledge with interaction data, then employs an adaptive noise pruning module to eliminate irrelevant entities. Subsequently, it constructs distinct Interaction and Preference KG views and aligns their embeddings within a shared representation space through contrastive learning, thereby drawing semantically related items closer. To further remove noise without ground-truth labels, a KG bottleneck mechanism optimizes the information flow, retaining only knowledge most relevant to the recommendation task. Finally, a multi-task learning strategy jointly optimizes recommendation loss, contrastive alignment, and denoising objectives. Extensive experiments on three public service recommendation datasets show consistent improvements in top-$K$ metrics (e.g., an average Recall@$K$ increase of nearly 6.55% on Alibaba-iFashion dataset), demonstrating MSKD’s effectiveness in denoising and its impact on enhancing metaverse service recommendations.

External IDs:dblp:journals/tsc/SunLWKTCX25