Topological Interpretable Multi-scale Sequential Recommendation

Tao Yuan, Shuzi Niu, Huiyuan Li

Abstract: Sequential recommendation attempts to predict next items based on user historical sequences. However, items to be predicted next depend on user’s long, short or mid-term interest. The multi-scale modeling of user interest in an interpretable way poses a great challenge in sequential recommendation. Hence, we propose a topological data analysis based framework to model target items’ explicit dependency on previous items or item chunks with different time scales, which are easily changed into sequential patterns. First, we propose a topological transformation layer to map each user interaction sequence into persistent homology organized in a multi-scale interest tree. Then, this multi-scale interest tree is encoded to represent natural inclusion relations across scales through an recurrent aggregation process, namely tree aggregation block. Next, we add this block to the vanilla transformer, referred to as recurrent tree transformer, and utilize this new transformer to generate a unified user interest representation. The last fully connected layer is utilized to model the interaction between this unified representation and item embedding. Comprehensive experiments are conducted on two public benchmark datasets. Performance improvement on both datasets is averagely \(5\%\) over state-of-the-art baselines.