Beyond Alignment: Toward Structural Compatibility Between Semantic and Collaborative Spaces

Semantic information is widely used to complement collaborative signals in recommender systems, especially to address data sparsity, cold-start, and long-tail recommendation. Existing semantic–collaborative integration methods mainly focus on how to incorporate semantic information through enrichment, alignment, or disentanglement, while paying limited attention to what item–item relations are preserved after fusion. This question matters because the two views organize items differently: collaborative relations are derived from user behavior and shaped by item popularity, leaving tail items with few reliable collaborative neighbors, whereas semantic relations are derived from item content and can provide these items with meaningful neighbors. Consequently, fusion involves a structural trade-off, in which preserving more of one view's structure may weaken that of the other. To study this issue, we introduce structural compatibility, which characterizes how well the relational structures of both views are preserved after fusion, and quantify it through three measures: collaborative-neighborhood, semantic-neighborhood, and hierarchical semantic preservation. We further propose Structure-Preserving Fusion (SPF), which injects semantic information into the collaborative space through a controlled residual pathway and preserves semantic structure via hierarchy-aware regularization, thus avoiding global cross-view alignment. Experiments on three datasets show that SPF consistently improves recommendation performance, with larger gains for sparse interactions and tail items. Further analyses reveal that stronger semantic preservation can substantially weaken collaborative-neighborhood preservation without performance gains, indicating that effective fusion requires balancing the preservation of both structures. Overall, our findings establish structural compatibility as a complementary principle for analyzing and designing semantic–collaborative integration.