Octopus

Abstract

Candidate generation is a critical task for recommendation system, which is technically challenging from two perspectives. On the one hand, recommendation system requires the comprehensive inclusion of user's interested candidates, yet typical deep user modeling approaches would represent each user as an onefold vector, which is hard to capture user's diverse interests. On the other hand, for the sake of practicability, the candidate generation process needs to be both accurate and efficient. Although existing multi-channel structures'', like memory networks, are more capable of representing user's diverse interests, they may bring in substantial irrelevant candidates and lead to rapid growth of temporal cost. As a result, it remains a tough issue to comprehensively acquire user's interested items in a practical way. In this work, a novel personalized candidate generation paradigm, Octopus, is proposed, which is remarkable for its comprehensiveness and elasticity. Similar with those conventional multi-channel structures'', Octopus also generates multiple vectors for the comprehensive representation of user's diverse interests. However, Octopus' representation functions are formulated in a highly elastic way, whose scale and type are adaptively determined based on each user's individual background. Therefore, it will not only identify user's interested items comprehensively, but also rule out irrelevant candidates and help to maintain a feasible running cost. Extensive experiments are conducted with both industrial and publicly available datasets, where the effectiveness of Octopus is verified in comparison with the state-of-the-art baseline approaches.