One potential drawback of relying on compositional embedding for large-scale recommendation systems is the accuracy trade-off that comes with reducing the size of embedding tables. While techniques like double hashing can help decrease table capacity, they may lead to increased memory access or inefficient use of resources. The method necessitates reconstructing embeddings from separate Q and R tables, resulting in double-memory accesses that impact overall system performance.
Moreover, as shown in studies such as RecShard [13], there is a significant increase in model size over time due to incorporating more item attributes for better quality modeling. This growth poses challenges when running inference on single-node servers or introducing SSDs due to synchronization issues or negative impacts on execution time.
Additionally, compositional embedding may struggle with maintaining high accuracy levels when hash collision values are increased significantly to meet bandwidth demands between different types of memories within a heterogeneous system.