Recommender Transformers with Behavior Pathways

Sequential recommendation requires the recommender to capture the evolving behavior characteristics from logged user behavior data for accurate recommendations. However, user behavior sequences are viewed as a script with multiple ongoing threads intertwined. We find that only a small set of pivotal behaviors can be evolved into the user's future action. As a result, the future behavior of the user is hard to predict. We conclude this characteristic for sequential behaviors of each user as the Behavior Pathway. Different users have their unique behavior pathways. Among existing sequential models, transformers have shown great capacity in capturing global-dependent characteristics. However, these models mainly provide a dense distribution over all previous behaviors using the self-attention mechanism, making the final predictions overwhelmed by the trivial behaviors not adjusted to each user. In this paper, we build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. RETR can dynamically plan the behavior pathway specified for each user, and sparingly activate the network through this behavior pathway to effectively capture evolving patterns useful for recommendation. The key design is a learned binary route to prevent the behavior pathway from being overwhelmed by trivial behaviors. We empirically verify the effectiveness of RETR on seven real-world datasets and RETR yields state-of-the-art performance. Recent advanced sequential recommendation models, such as SASRec [16], Bert4Rec [28] and SMRec [6] , have achieved significant improvements. Transformers enable these models to recognize global-range sequential patterns, and to model how future behaviors are anchored in historical ones. The self-attention mechanism does make it possible to explore all previous behaviors of each user, with the whole neural network activated. However, misuse of user information, regardless of whether they are informative or not, floods models with trivial ones, makes models dense and inefficient, and results in key behaviors losing voice. And this clearly contradicts with the way our brain works. The human being has many different parts of the brain specialized for various tasks, yet the brain only calls upon the relevant pieces for a given situation [37] . To some extent, user behavior sequences can Preprint.