Previous studies on sequential recommendation (SR) have predominantly concentrated on optimizing recommendation accuracy. However, there remains a significant gap in enhancing recommendation diversity, particularly for short interaction sequences. The limited availability of interaction information in short sequences hampers the recommender’s ability to comprehensively model users’ intents, consequently affecting both the diversity and accuracy of recommendation. In light of the above challenge, we propose reTrospective and pRospective Transformers for dIversified sEquential Recommendation (TRIER). The TRIER addresses the issue of insufficient information in short interaction sequences by first retrospectively learning to predict users’ potential historical interactions, thereby introducing additional information and expanding short interaction sequences, and then capturing users’ potential intents from multiple augmented sequences. Finally, the TRIER learns to generate diverse recommendation lists by covering as many potential intents as possible.

To evaluate the effectiveness of TRIER, we conduct extensive experiments on three benchmark datasets. The experimental results demonstrate that TRIER significantly outperforms state-of-the-art methods, exhibiting diversity improvement of up to 11.36% in terms of intra-list distance (ILD@5) on the Steam dataset, 3.43% ILD@5 on the Yelp dataset and 3.77% in terms of category coverage (CC@5) on the Beauty dataset. As for accuracy, on the Yelp dataset, we observe notable improvement of 7.62% and 8.63% in HR@5 and NDCG@5, respectively. Moreover, we found that TRIER reveals more significant accuracy and diversity improvement for short interaction sequences.

先前关于顺序推荐（SR）的研究主要集中在优化推荐准确性上。然而，在增强推荐多样性方面仍然存在很大差距，特别是对于短交互序列。短序列中交互信息的有限可用性阻碍了推荐器全面建模用户意图的能力，从而影响推荐的多样性和准确性。鉴于上述挑战，我们提出了多样化顺序推荐的回顾性和前瞻性变压器（TRIER）。 TRIER解决了短交互序列中信息不足的问题，首先回顾性地学习预测用户潜在的历史交互，从而引入附加信息并扩展短交互序列，然后从多个增强序列中捕获用户的潜在意图。最后，TRIER 学习通过覆盖尽可能多的潜在意图来生成多样化的推荐列表。

为了评估 TRIER 的有效性，我们对三个基准数据集进行了广泛的实验。实验结果表明，TRIER 显着优于最先进的方法，在 Steam 数据集上的列表内距离 (ILD@5) 方面表现出高达 11.36% 的多样性改进，在 Yelp 上的 ILD@5 方面表现出高达 3.43% 的多样性改进美容数据集的类别覆盖率 (CC@5) 为 3.77%。至于准确性，在 Yelp 数据集上，我们观察到 HR@5 和 NDCG@5 分别显着提高了 7.62% 和 8.63%。此外，我们发现 TRIER 对于短交互序列显示出更显着的准确性和多样性改进。