Explanation as a Defense of Recommendation
Explanation as a Defense of Recommendation
复制标题
DOI：
10.1145/3437963.3441726
复制
发表时间：
2021-01
期刊：
影响因子：
--
通讯作者：
Aobo Yang;Nan Wang;Hongbo Deng;Hongning Wang
中科院分区：
文献类型：
--
作者：
Aobo Yang;Nan Wang;Hongbo Deng;Hongning Wang
事实证明，文本解释有助于提高用户对机器推荐的满意度。然而，当前的主流解决方案将解释学习与推荐学习松散地联系起来：例如，它们通常分别建模为评分预测和内容生成任务。在这项工作中，我们建议通过强化推荐与其相应解释之间的情感一致性的想法来加强它们的联系。在训练时，两个学习任务通过潜在情感向量连接起来，该向量由推荐模块编码并用于为解释生成做出单词选择。在训练和推理时，解释模块都需要生成与推荐模块预测的情绪相匹配的解释文本。大量的实验表明，我们的解决方案在推荐和解释任务方面都优于一组丰富的基线，特别是在提高其生成的解释质量方面。更重要的是，我们的用户研究证实我们生成的解释可以帮助用户更好地识别推荐项目之间的差异并理解推荐项目的原因。
Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.