多行为推荐系统旨在利用用户多种行为的交互数据来提升系统的推荐性能. 现有的多行为推荐方法通常将多行为数据直接作用于共享的初始化用户表征上, 并在任务中糅合了对用户偏好的挖掘和对不同行为间联系的建模. 然而, 这些算法忽视了不同交互行为中存在的数据不平衡问题(不同行为交互数据量差别较大)以及适配上述两种任务而引起的信息损失问题. 事实上, 用户偏好是指用户在不同行为中表现出来的喜好(例如, 浏览的喜好), 而各行为间的联系表现为用户偏好在不同行为间潜在的转化关系(例如, 浏览转换为购买). 在多行为推荐中, 对用户偏好的挖掘和对行为间联系的建模可以看作两个阶段的任务. 基于上述讨论, 提出基于两阶段学习的多行为推荐. 两阶段策略设计的优势是解耦了前述两种任务. 两阶段策略采取固定参数交替训练的方式实现, 并同时保留了模型端到端的结构. 1)第1阶段专注于不同行为下的用户偏好建模: 先利用所有交互数据(不区分行为类别)对用户的全局喜好进行建模, 以最大程度缓解数据稀疏性问题, 再分别利用各行为的交互数据细化该特定行为下的用户偏好(局部偏好), 以减轻不同行为之间的数据不平衡问题造成的影响. 2)第2阶段专注于对不同行为间联系的建模, 通过解耦对用户偏好的挖掘和对不同行为间联系的建模, 以缓解因适配两种任务而引起的信息损失问题. 这种两阶段模式能够显著提升系统对目标行为的预测能力. 大量实验结果表明, 所提模型在性能上远优于最先进的基线模型, 在Tmall和Beibei两个真实基准数据集上的性能提升平均可以达到103.01%和33.87%.

Multi-behavior recommendation aims to utilize interactive data from multiple behaviors of users to improve recommendation performance. Existing multi-behavior recommendation methods generally directly exploit the multi-behavior data for the shared initialized user representations and involve the mining of user preferences and modeling of relationships among different behaviors in the tasks. However, these methods ignore the data imbalance under different interactive behaviors (the amount of interactive data varies greatly among different behaviors) and the information loss caused by the adaptation to the above two tasks. User preferences refer to the interests that users exhibit in different behaviors (e.g., browsing preferences), and the relationship among behaviors indicates a potential conversion from one behavior to another behavior (e.g., the conversion from browsing to purchasing). In multi-behavior recommendation, the mining of user preferences and the modeling of relationships among different behaviors can be regarded as a two-stage task. On the basis of the above considerations, the model of two-stage learning for multi-behavior recommendation (TSL-MBR for short) is proposed, which decouples the above two tasks with a two-stage strategy. In particular, the model retains the end-to-end structure and learns the two tasks by alternating training with fixed parameters. The first stage is to model user preferences under different behaviors. In this stage, the interactive data from all behaviors (without distinction as to behavior type) are first used to model the global preferences of users to alleviate the problem of data sparsity to the greatest extent. Then, the interactive data of each behavior are used to refine the behavior-specific user preference (local preference) and thus lessen the influence of the data imbalance among different behaviors. The second stage is to model the relationships among different behaviors. In this stage, the mining of user preferences and modeling of relationships among different behaviors are decoupled to relieve the information loss problem caused by adaptation to the two tasks. This two-stage model significantly improves the system’s ability to predict target behaviors. Extensive experimental results show that TSL-MBR can substantially outperform the state-of-the-art baseline models, achieving 103.01% and 33.87% of relative gains on average over the best baseline on the Tmall and Beibei datasets, respectively.

国家自然科学基金(62272254,61976042,61972068);兴辽英才计划(XLYC2007023);辽宁省高等学校创新人才支持计划(LR2019020)