DCRNN: A Deep Cross approach based on RNN for Partial Parameter Sharing in Multi-task Learning

TL;DR

This paper introduces DCRNN, a novel multi-task learning model for recommendation systems that uses RNN-based cross networks and partial parameter sharing to improve predictive performance and efficiency.

Contribution

The paper proposes a new recommendation model with RNN-based cross processing and partial parameter sharing, addressing limitations of existing multi-task learning architectures.

Findings

Improves the expressive ability of multi-task recommendation models.

Effectively captures potential correlations between tasks.

Reduces computational costs compared to traditional models.

Abstract

In recent years, DL has developed rapidly, and personalized services are exploring using DL algorithms to improve the performance of the recommendation system. For personalized services, a successful recommendation consists of two parts: attracting users to click the item and users being willing to consume the item. If both tasks need to be predicted at the same time, traditional recommendation systems generally train two independent models. This approach is cumbersome and does not effectively model the relationship between the two subtasks of "click-consumption". Therefore, in order to improve the success rate of recommendation and reduce computational costs, researchers are trying to model multi-task learning. At present, existing multi-task learning models generally adopt hard parameter sharing or soft parameter sharing architecture, but these two architectures each have certain problems. Therefore, in this work, we propose a novel recommendation model based on real recommendation scenarios, Deep Cross network based on RNN for partial parameter sharing (DCRNN). The model has three innovations: 1) It adopts the idea of cross network and uses RNN network to cross-process the features, thereby effectively improves the expressive ability of the model; 2) It innovatively proposes the structure of partial parameter sharing; 3) It can effectively capture the potential correlation between different tasks to optimize the efficiency and methods for learning different tasks.