Conversational Product Search Based on Negative Feedback
Conversational Product Search Based on Negative Feedback
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DOI：
10.1145/3357384.3357939
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发表时间：
2019-09
期刊：
影响因子：
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通讯作者：
Keping Bi;Qingyao Ai;Yongfeng Zhang;W. Bruce Croft
中科院分区：
文献类型：
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作者：
Keping Bi;Qingyao Ai;Yongfeng Zhang;W. Bruce Croft
智能助手改变了人们与计算机的交互方式，使人们在有购买需求时可以通过对话搜索产品。在交互过程中，系统可以就理想产品的某些方面提出问题，以澄清用户的需求。例如，以前的工作建议在显示结果之前询问用户理想项目的确切特征。然而，用户可能不清楚理想的物品是什么样子的，特别是当他们没有看到任何物品时。因此，通过展示示例项目并请求反馈来促进会话搜索更可行。此外，当用户对所呈现的项目提供负面反馈时，更容易收集他们对非相关项目的某些属性（方面-值对）的详细反馈。通过将项目级的负面反馈分解为方面-值对上的细粒度反馈，可以获得更多的信息来帮助澄清用户的意图。因此，在本文中，我们提出了一个会话范式的产品搜索驱动的不相关的项目，在此基础上细粒度的反馈收集和利用，以显示更好的结果在下一次迭代。然后，我们提出了一个方面值的可能性模型，将积极和消极的反馈细粒度的方面值对的非相关项目。实验结果表明，我们的模型是显着优于国家的最先进的产品搜索基线没有使用反馈和那些基线使用项目级的负反馈。
Intelligent assistants change the way people interact with computers and make it possible for people to search for products through conversations when they have purchase needs. During the interactions, the system could ask questions on certain aspects of the ideal products to clarify the users' needs. For example, previous work proposed to ask users the exact characteristics of their ideal items before showing results. However, users may not have clear ideas about what an ideal item looks like, especially when they have not seen any item. So it is more feasible to facilitate the conversational search by showing example items and asking for feedback instead. In addition, when the users provide negative feedback for the presented items, it is easier to collect their detailed feedback on certain properties (aspect-value pairs) of the non-relevant items. By breaking down the item-level negative feedback to fine-grained feedback on aspect-value pairs, more information is available to help clarify users' intents. So in this paper, we propose a conversational paradigm for product search driven by non-relevant items, based on which fine-grained feedback is collected and utilized to show better results in the next iteration. We then propose an aspect-value likelihood model to incorporate both positive and negative feedback on fine-grained aspect-value pairs of the non-relevant items. Experimental results show that our model is significantly better than state-of-the-art product search baselines without using feedback and those baselines using item-level negative feedback.