Adaptive Utilization of Cross-scenario Information for Multi-scenario Recommendation

TL;DR

This paper introduces a unified cross-scenario recommendation model that adaptively leverages transferable features and cross-scenario knowledge, improving performance especially in data-sparse scenarios.

Contribution

It proposes a novel Cross-Scenario Information Interaction (CSII) model with an attention-based aggregator and feature selection, addressing data imbalance and negative transfer issues in multi-scenario recommendation.

Findings

Achieved superior performance on production datasets.

Online A/B test showed a 1.0% GMV increase.

Effectively handles data sparsity and scenario differences.

Abstract

Recommender system of the e-commerce platform usually serves multiple business scenarios. Multi-scenario Recommendation (MSR) is an important topic that improves ranking performance by leveraging information from different scenarios. Recent methods for MSR mostly construct scenario shared or specific modules to model commonalities and differences among scenarios. However, when the amount of data among scenarios is skewed or data in some scenarios is extremely sparse, it is difficult to learn scenario-specific parameters well. Besides, simple sharing of information from other scenarios may result in a negative transfer. In this paper, we propose a unified model named Cross-Scenario Information Interaction (CSII) to serve all scenarios by a mixture of scenario-dominated experts. Specifically, we propose a novel method to select highly transferable features in data instances. Then, we propose an attention-based aggregator module, which can adaptively extract relative knowledge from cross-scenario. Experiments on the production dataset verify the superiority of our method. Online A/B test in Meituan Waimai APP also shows a significant performance gain, leading to an average improvement in GMV (Gross Merchandise Value) of 1.0% for overall scenarios.