arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2408.00799cs.IRcs.LGstat.ML
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推荐系统中用于索引构建与检索的深度不确定性探索

Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System

Shopee Discovery Ads（Shopee 探索广告部门）

Xin Jiang, Kaiqiang Wang, Yinlong Wang, Fengchang Lv, Taiyang Peng, Shuai Yang, Xianteng Wu, Pengye Zhang, Shuo Yuan, Yifan Zeng

备注：
accepted by cikm2024
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AI总结：

针对推荐匹配阶段相关性与新颖性难以兼顾的问题，提出 UICR 算法，通过联合建模模型与索引不确定性并融合相关性、不确定性分数，在工业环境和开源数据集上提升新颖性且不损失相关性。

摘要：

在推荐系统中，最终结果的相关性和新颖性是通过“匹配 -> 排序 -> 策略”的级联系统来选择的。匹配模型作为该流程的起点，决定了后续阶段的上限。平衡匹配结果的相关性与新颖性是推荐系统设计和优化中的关键步骤，对提升推荐质量具有重要作用。然而，典型的匹配算法尚未同时完美解决相关性和新颖性问题。一个主要原因是，深度匹配算法在估计长尾物品（例如由于训练样本不足）时表现出显著的不确定性。这种不确定性不仅影响模型训练，还会影响这些模型在索引构建和 beam search 检索过程中的置信度。本文提出 UICR（Uncertainty-based explore for Index Construction and Retrieval，基于不确定性的索引构建与检索探索）算法，在匹配阶段引入不确定性建模的概念，实现对模型不确定性和索引不确定性的多任务建模。最终匹配结果通过结合模型推断出的相关性分数与不确定性分数获得。实验结果表明，UICR 在真实工业生产环境和多个开源数据集上均能在不牺牲相关性的情况下提升新颖性。值得注意的是，Shopee 展示广告的在线 A/B 测试结果证明了所提算法的有效性。

英文摘要：

In recommendation systems, the relevance and novelty of the final results are selected through a cascade system of Matching -> Ranking -> Strategy. The matching model serves as the starting point of the pipeline and determines the upper bound of the subsequent stages. Balancing the relevance and novelty of matching results is a crucial step in the design and optimization of recommendation systems, contributing significantly to improving recommendation quality. However, the typical matching algorithms have not simultaneously addressed the relevance and novelty perfectly. One main reason is that deep matching algorithms exhibit significant uncertainty when estimating items in the long tail (e.g., due to insufficient training samples) items.The uncertainty not only affects the training of the models but also influences the confidence in the index construction and beam search retrieval process of these models. This paper proposes the UICR (Uncertainty-based explore for Index Construction and Retrieval) algorithm, which introduces the concept of uncertainty modeling in the matching stage and achieves multi-task modeling of model uncertainty and index uncertainty. The final matching results are obtained by combining the relevance score and uncertainty score infered by the model. Experimental results demonstrate that the UICR improves novelty without sacrificing relevance on realworld industrial productive environments and multiple open-source datasets. Remarkably, online A/B test results of display advertising in Shopee demonstrates the effectiveness of the proposed algorithm.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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