DiffuRec:顺序推荐的扩散模型

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zihao Li, Aixin Sun, Chenliang Li
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引用次数: 0

摘要

顺序推荐的主流解决方案是用固定向量表示项目。这些向量在捕捉项目的潜在方面和用户的不同偏好方面能力有限。作为一种新的生成范式,扩散模型在计算机视觉和自然语言处理等领域取得了优异的表现。据我们了解,它在表征生成方面的独特优点非常适合顺序推荐的问题设置。在本文中,我们首次尝试将扩散模型应用于顺序推荐,并提出了用于项目表示构建和不确定性注入的 DiffuRec。在 DiffuRec 中,我们不再将项目表示建模为固定向量,而是将其表示为分布,从而自适应地反映用户的多种兴趣和项目的各个方面。在扩散阶段,DiffuRec 通过添加噪声将目标项目嵌入破坏为高斯分布,并进一步应用于顺序项目分布表示的生成和不确定性注入。然后,将项目表示输入近似器,以重建目标项目表示。在反向阶段,根据用户的历史交互行为,我们将高斯噪声反向引入目标项目表示,然后应用舍入操作进行目标项目预测。在四个数据集上进行的实验表明,DiffuRec 的性能远远优于强基线1。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DiffuRec: A Diffusion Model for Sequential Recommendation

Mainstream solutions to sequential recommendation represent items with fixed vectors. These vectors have limited capability in capturing items’ latent aspects and users’ diverse preferences. As a new generative paradigm, diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this article, we make the very first attempt to adapt the diffusion model to sequential recommendation and propose DiffuRec for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect a user’s multiple interests and an item’s various aspects adaptively. In the diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterward, the item representation is fed into an approximator for target item representation reconstruction. In the reverse phase, based on a user’s historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin.1

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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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