Multi-type itemset embedding for learning behavior success

Abstract

Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face diculties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of dierent types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, “multi-type itemset embedding”, to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) nding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both eectiveness and ecency of the proposed framework.