Semantic-Guided Structural information Borrowing for Item Cold-Start Recommendation on Graphs

In graph-based recommender systems, items are ranked by embeddings learned from the structural information of the user-item interaction graph. A cold (new) item has no interaction history and is isolated in this graph, so it lacks such structural information and cannot be ranked effectively like warm items. Existing methods address this issue in two ways. The first is injecting content features, which provide a cold item with a semantic embedding, but not the structural embedding required for ranking. The second is complementing the structural information through simulated or reconstructed edges. Since these edges are generated instead of real, the complemented structural information may be noisy. To overcome these limitations, we propose an end-to-end Semantic-Guided Structural information Borrowing framework (SSB), which allows a cold item to borrow the structural information from its similar warm items to represent itself, thus the cold item obtains a structural embedding for accurate recommendation. Specifically, we use a fine-tuning-free LLM to help a cold item to accurately locate the similar warm items, ensuring that the borrowed structural information is indeed relevant. Then, we propose a soft-structure borrowing mechanism composed of a soft attention mechanism and hierarchical structure aggregation. This mechanism enables a cold item to flexibly determine how much information to borrow from each located similar warm item, thereby making a cold item obtain a continuous combination of warm item structural embeddings rather than embeddings learned from discrete edges. Moreover, we optimize a pseudo-cold mask method to address the lack of supervision signals caused by missing structural information of cold items, letting gradients flow back to the learnable parameters during training. Finally, we conduct extensive experiments on three Amazon datasets: Sports, Toys, and Beauty. The results show that SSB outperforms SOTA cold-start baselines on Sports and Toys, and is comparable to CGRC on Beauty.