Leverage Implicit Feedback for Context-aware Product Search
Leverage Implicit Feedback for Context-aware Product Search
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发表时间：
2019-09
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通讯作者：
Keping Bi;C. Teo;Yesh Dattatreya;Vijai Mohan;W. Bruce Croft
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作者：
Keping Bi;C. Teo;Yesh Dattatreya;Vijai Mohan;W. Bruce Croft
产品搜索是网上购物的一个重要入口。与网络搜索相比，产品搜索的检索结果不仅需要相关，而且应该满足客户的偏好，以引发购买。以往的研究已经证明了购买历史在个性化产品搜索中的有效性。然而，很少或没有购买历史的客户无法从个性化产品搜索中受益。此外，从客户购买历史中提取的偏好通常是长期的，可能并不总是与她的短期兴趣一致。因此，在本文中，我们利用查询会话中的点击作为隐式反馈来表示用户的隐藏意图，这进一步作为对查询的后续结果页面重新排序的基础。在推荐任务中使用隐式反馈对用户偏好进行建模已经得到了广泛的研究。然而，关于用户对产品搜索的短期兴趣建模的研究很少。我们研究短期上下文是否有助于在以下查询结果页面中推广用户的理想项目。此外，我们提出了一个端到端的上下文感知嵌入模型，该模型可以捕获长期和短期上下文依赖关系。我们在一个商业产品搜索引擎的搜索日志数据集上的实验结果表明，与长期和无上下文相比，短期上下文导致了更好的性能。我们的研究结果还表明，我们提出的模型比基于单词的上下文感知模型更有效。
Product search serves as an important entry point for online shopping. In contrast to web search, the retrieved results in product search not only need to be relevant but also should satisfy customers' preferences in order to elicit purchases. Previous work has shown the efficacy of purchase history in personalized product search. However, customers with little or no purchase history do not benefit from personalized product search. Furthermore, preferences extracted from a customer's purchase history are usually long-term and may not always align with her short-term interests. Hence, in this paper, we leverage clicks within a query session, as implicit feedback, to represent users' hidden intents, which further act as the basis for re-ranking subsequent result pages for the query. It has been studied extensively to model user preference with implicit feedback in recommendation tasks. However, there has been little research on modeling users' short-term interest in product search. We study whether short-term context could help promote users' ideal item in the following result pages for a query. Furthermore, we propose an end-to-end context-aware embedding model which can capture long-term and short-term context dependencies. Our experimental results on the datasets collected from the search log of a commercial product search engine show that short-term context leads to much better performance compared with long-term and no context. Our results also show that our proposed model is more effective than word-based context-aware models.