Jian Liu
ABSTRACT
Recommender Systems have been proven to be of great business value in e-commerce platforms and recommendation algorithms play an important role in it. E-commerce platforms provide entrances for customers to enter channels that can meet their specific shopping needs. Trigger items displayed on entrance icons are useful to attract more entering. User instant interest can be explicitly induced by the trigger items and follow-up related target items are recommended accordingly, this recommendation scenario is called trigger-induced recommendation. However, existing trigger-induced recommendation algorithms usually optimize a single task (e.g., click-through rate prediction) based on users’ historical click sequences, and do not consider modeling the similarity between the trigger item and the target item. In this paper, we propose a novel recommendation method named Deep Multifaceted Highlight Network (DMHN) that can model users’ multiple types of behavior sequences simultaneously for multi-objective ranking in trigger-induced recommendation. DMHN utilizes the Multi-Interest Module to model user’s various behavior sequences to capture the different latent user interests, the Similarity Module to focus on the trigger item by modeling the similarity between the target item and the trigger item, and the Progressive Layered Extraction to optimize multiple objectives. Experiments on JD.com real production dataset demonstrate the superiority of DMHN over state-of-the-art methods.
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Index Terms
- Deep Multifaceted Highlight Network for Multi-objective Ranking in Trigger-Induced Recommendation
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