Flexible Order Aware Sequential Recommendation

Mingda Qian, Xiaoyan Gu, Lingyang Chu, Feifei Dai, Haihui Fan, Borang Li
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引用次数: 2

Abstract

Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.
灵活的顺序感知顺序推荐
顺序推荐可以对用户的兴趣进行动态建模,由于用户的兴趣可能会随着时间的变化而迅速变化,因此具有很大的价值。传统的顺序推荐方法假设用户行为是严格有序和顺序依赖的。然而,一些用户行为具有灵活的顺序,这意味着这些行为可以以任何顺序发生,并且不依赖于顺序。因此,传统方法可能基于错误的依赖关系捕获不准确的用户兴趣。受此启发,几种方法通过连续性或相似性来识别柔性顺序。然而,这些方法不能全面地理解柔性订单的本质,因为连续性或相似性并不能决定订单的柔性。因此,这些方法可能会错误地识别灵活的订单,从而导致不适当的建议。为了解决这些问题,我们提出了一种柔性订单感知顺序推荐(FOSR)方法来全面识别柔性订单。我们认为订单的灵活性与项目对共现的频率高度相关。因此,FOSR采用基于概率的柔性订单评估模块来模拟商品对频率,从而推断出准确的订单灵活性。频率标注模块从真实的商品对频率中提取标签来指导订单灵活性的测量。在测量订单灵活性的前提下,我们开发了一个灵活的订单感知自关注模块来全面建模灵活订单的依赖关系,并有效地学习动态用户兴趣。在四个基准数据集上的大量实验表明,我们的模型优于各种最先进的顺序推荐方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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