基于知识增强对比学习的长尾用户序列推荐算法

Sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning

序列推荐根据目标用户的历史交互序列,预测其可能感兴趣的下一个物品。现有的序列推荐方法虽然可以有效捕获用户的历史交互序列中的长期依赖关系,但是无法为交互序列较短且用户数量庞大的长尾用户提供精确推荐。为了解决此问题,提出了一种基于知识增强对比学习的长尾用户序列推荐算法。首先,基于知识图谱中的丰富实体关系信息,构建一个基于语义的物品相似度度量,分别提取原始序列中物品的协同关联物品。然后,基于不同学习序列提出2种序列增强算子,通过增强自监督信号解决长尾用户序列训练数据不足的问题。最后,通过对比自监督任务和推荐主任务的网络参数共享的联合训练,为长尾用户提供更精确的序列推荐结果。在实际数据集上的实验结果表明,所提算法可以有效提高针对长尾用户的序列推荐精度。

Sequential recommendation predicts next items for users based on their historical interactions.Existing methods capture long-term dependencies but struggle to recommend precisely for users with short interaction sequences,especially for long-tail users.Therefore,a sequential recommendation algorithm for long-tail users based on knowledge-enhanced contrastive learning was proposed.Firstly,semantic item similarity was introduced by leveraging relationships between entities in the knowledge graph to extract correlated items from original sequences.Secondly,two sequence augmentation operators were proposed based on different contrastive learning views,addressing the problem of insufficient training for long-tail user sequences by augmenting self-supervised signals.Finally,precise sequence recommendations were provided for long-tail users by utilizing the joint training of shared network parameters between contrastive self-supervised tasks and the recommendation task.Experimental results on real-world datasets demonstrate the effectiveness of the proposed algorithm in improving performance for long-tail users.