Deep Reinforcement Learning for Personalized Search Story Recommendation

TL;DR

This paper proposes a deep reinforcement learning approach for personalized search story recommendation, effectively modeling immediate and future user engagement to improve relevance and user experience.

Contribution

It introduces a novel deep reinforcement learning framework combining imitation and reinforcement learning for personalized search story recommendation.

Findings

Outperforms baseline methods on real-world datasets

Effectively models cross-channel effects and user preferences

Improves user engagement and relevance in search stories

Abstract

In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms. The recommended search story guides a user to identify her own preference and personal intent, which subsequently influences the user's real-time and long-term search behavior. %With such an increased importance of search stories, As search stories become increasingly important, in this work, we study the problem of personalized search story recommendation within a search engine, which aims to suggest a search story relevant to both a search keyword and an individual user's interest. To address the challenge of modeling both immediate and future values of recommended search stories (i.e., cross-channel effect), for which conventional supervised learning framework is not applicable, we resort to a Markov decision process and propose a deep reinforcement learning architecture trained by both imitation learning and reinforcement learning. We empirically demonstrate the effectiveness of our proposed approach through extensive experiments on real-world data sets from JD.com.