Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations
Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations
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DOI：
10.1145/3539618.3591787
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发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
Zhe Fu;Xi Niu;Li Yu
中科院分区：
文献类型：
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作者：
Zhe Fu;Xi Niu;Li Yu
Serendipity是一个意想不到但有价值的发现的概念。由于其难以捉摸和主观的性质，即使在机器学习和深度学习技术取得进步的今天，偶然性也很难研究。地面实况数据的收集和模型的建立是一个开放性的研究问题。本文讨论了在推荐系统中识别偶然发现的数据和模型挑战。对于地面实况数据收集，它提出了一种新的和可扩展的方法，通过使用用户生成的评论和众包方法。其结果是一个大规模的地面真相数据的意外发现。对于模型开发，它设计了一个自增强模块来学习偶然性的细粒度方面，以减轻任何偶然性地面实况数据集中固有的数据稀疏问题。自我增强模块足够通用，可以与许多基础深度学习模型一起应用。进行了一系列的实验。因此，在我们收集的地面真实数据上训练的基础深度学习模型，以及在自我增强模块的帮助下，在预测意外发现方面优于最先进的基线模型。
Serendipity is a notion that means an unexpected but valuable discovery. Due to its elusive and subjective nature, serendipity is difficult to study even with today's advances in machine learning and deep learning techniques. Both ground truth data collecting and model developing are the open research questions. This paper addresses both the data and the model challenges for identifying serendipity in recommender systems. For the ground truth data collecting, it proposes a new and scalable approach by using both user generated reviews and a crowd sourcing method. The result is a large-scale ground truth data on serendipity. For model developing, it designed a self-enhanced module to learn the fine-grained facets of serendipity in order to mitigate the inherent data sparsity problem in any serendipity ground truth dataset. The self-enhanced module is general enough to be applied with many base deep learning models for serendipity. A series of experiments have been conducted. As the result, a base deep learning model trained on our collected ground truth data, as well as with the help of the self-enhanced module, outperforms the state-of-the-art baseline models in predicting serendipity.