多任务推荐算法研究综述

Survey of Multi-task Recommendation Algorithms

单任务推荐算法存在数据稀疏、冷启动和推荐效果不稳定等问题。多任务推荐算法可以将多种类型的用户行为数据和额外信息进行联合建模,从而更好地挖掘用户的兴趣和需求,以提高推荐效果和用户满意度,为解决单任务推荐算法存在的一系列问题提供了新思路。首先,梳理了多任务推荐算法的发展背景与趋势。其次,介绍了多任务推荐算法的实现步骤以及构建原则,并阐述了多任务学习具有数据增强、特征识别、特征互补和正则化效应等优势。然后,对不同共享模式的多任务学习方法在推荐算法中的应用进行了介绍,并对部分经典模型的优缺点及任务之间的关系进行了归纳总结。接着,介绍了多任务推荐算法常用的数据集和评估指标,并阐述了与其他推荐算法在数据集合评估指标方面的区别和联系。最后,指出多任务学习存在负迁移、参数优化冲突、可解释性差等不足,对多任务推荐算法与强化学习、凸函数优化方法、异构信息网络相结合进行了展望。

Single-task recommendation algorithms have problems such as sparse data,cold start and unstable recom-mendation effect.Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information,to better explore the user’s interests and needs in order to improve the recommendation effect and user satisfaction,which provides a new way of thinking to solve a series of problems existing in single-task recommendation algorithms.Firstly,the development background and trend of multi-task recommendation algo-rithms are sorted out.Secondly,the implementation steps of the multi-task recommendation algorithm and the con-struction principle are introduced,and the advantages of multi-task learning with data enhancement,feature identifi-cation,feature complementation and regularization effect are elaborated.Then,the application of multi-task learning methods in recommendation algorithms with different sharing models is introduced,and the advantages and disad-vantages of some classical models and the relationship between tasks are summarized.Then,the commonly used datasets and evaluation metrics for multi-task recommendation algorithms are introduced,and the differences and connections with other recommendation algorithms in terms of dataset evaluation metrics are elaborated.Finally,it is pointed out that multi-task learning has shortcomings such as negative migration,parameter optimization conflicts,poor interpretability,etc.,and an outlook is given to the combination of multi-task recommendation algorithms with reinforcement learning,convex function optimization methods,and heterogeneous information networks.