Query-dominant User Interest Network for Large-Scale Search Ranking

Abstract

Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.

10 pages

References in corpus (11)

• BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
• Disentangling Long and Short-Term Interests for Recommendation
• Learning Latent Vector Spaces for Product Search
• A Zero Attention Model for Personalized Product Search
• A Transformer-based Embedding Model for Personalized Product Search
• Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
• Learning a Fine-Grained Review-based Transformer Model for Personalized Product Search
• Explainable Product Search with a Dynamic Relation Embedding Model
• When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation
• Why Don't You Click: Neural Correlates of Non-Click Behaviors in Web Search
• Search Behavior Prediction: A Hypergraph Perspective

Cited by in corpus (1)

• Unconstrained Monotonic Calibration of Predictions in Deep Ranking Systems