Deep Reinforcement Learning for Page-wise Recommendations

TL;DR

This paper introduces DeepPage, a deep reinforcement learning framework that optimizes page-wise recommendations by generating item sets and display strategies based on real-time user feedback, improving e-commerce recommendations.

Contribution

The paper presents a novel deep reinforcement learning approach for joint generation and display of recommended items in a page-wise manner, addressing real-time feedback challenges.

Findings

DeepPage outperforms traditional methods on real-world datasets.

The framework effectively adapts to user feedback in real-time.

Experimental results show significant improvement in recommendation quality.

Abstract

Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is -- users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems -- (1) how to update recommending strategy according to user's \textit{real-time feedback}, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.