动态层次Transformer序列推荐算法

序列化推荐任务根据用户历史行为序列,预测下一时刻即将交互的物品.大量研究表明:预测物品对用户历史行为序列的依赖是多层次的.已有的多尺度方法是针对隐式表示空间的启发式设计,不能显式地推断层次结构.为此,该文提出动态层次Transformer,来同时学习多尺度隐式表示与显式层次树.动态层次Transformer采用多层结构...

Dynamic Hierarchical Transformer for Sequential Recommendation

Sequential recommendation aims at predicting the items to be interacted next time according to the user's historical behavior sequences. Most related studies have shown that items to be interacted depend on different scales of blocks in historical sequences. Implicit multi-scale representation space is often heuristically designed, from which we cannot infer an explicit hierarchy. Therefore, we propose a Dynamic hierarchical Transformer to learn multi-scale implicit representation and explicit hierarchy simultaneously. The Dynamic hierarchical Transformer adopts a multi-layer structure with dynamically generated mask matrices from neighbor block attention per layer in a bottom-up manner. In the derived multi-scale hierarchy, the composition structure per layer is inferred from the block mask matrix and the implicit representation per scale is obtained by the dynamic block mask and self-attention mechanism. Experimental results on two benchmark datasets (MovieLens-100k and Amazon Movies and TV) show that our proposed model improves the precision by 2.09% and 5.43% respectively over the state-of-the-art baselines. Furthermore, the derived multi-scale hierarchy agrees with our intuition through the case study.