面向移动应用评分推荐的多任务图嵌入深度预测模型

Multi-task Graph-embedding Deep Prediction Model for Mobile App Rating Recommendation

随着智能终端设备以及移动应用软件的普及,用户对应用质量的要求和用户体验需要愈发凸显。移动应用的评分推荐作为一项有效的事前评估手段,逐渐得到市场关注。传统的应用评分推荐工作主要围绕解决数据稀疏和模型深度问题,未能对应用推荐本身的图结构、多任务形态进行准确表征。针对该问题,提出了一种面向移动应用评分推荐的图嵌入多任务模型AppGRec,利用归纳型二部图的嵌入结构对特征中的用户交互关系进行挖掘,并使用shared-bottom多任务模型捕获应用评分中的多任务特点,同时兼顾了数据稀疏和模型深度的影响。在Google Play上收集了16 031个有效的移动应用及其特征数据作为验证数据集,实验结果表明,AppGRec在MAE和RMSE上相比state-of-the-art模型分别提升了10.4%和10.9%。此外,对AppGRec超参和核心模块的影响做了具体分析,多角度验证其有效性。

With the prevalence of smart terminal devices and mobile application(app for short),the requirements for application quality and user experience gradually increase.As an effective pre-assessment method,mobile app rating recommendation has gained increasing attention from app markets.The traditional app rating and recommendation works mainly focus on challenges such as data sparsity and model depth.Nevertheless,they fail to accurately capture the graph relationship within the apps and users.Furthermore,the multi-task characteristic of app recommendation is neglected.Aiming at these shortcomings,this paper proposes a graph embedding multi-task model AppGRec for mobile app rating and recommendation.AppGRec uses the embedding structure of inductive bipartite graph to mine the user interaction features.It uses the shared-bottom based model to capture the multi-task feature in app rating,while considering the effects of data sparsity and model depth.16031 valid mobile apps and their feature data on Google Play are collected as dataset for method evaluation.Experimental results show that AppGRec achieves 10.4%and 10.9%improvement in terms of MAE and RMSE respectively comparing with the state-of-the-art models.In addition,this paper also makes quantitative analysis of the impact of hyperparameters and some core modules in AppGRec,and verifies the effectiveness from multiple perspectives.