Choon Hui Teo

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 [3]. However, customers with little or no purchase history do not benefit from personalized product search.

In product search, users tend to browse results on multiple search result pages (SERPs) (e.g., for queries on clothing and shoes) before deciding which item to purchase. Users’ clicks can be considered as implicit feedback which indicates their preferences and used to re-rank subsequent SERPs. Relevance feedback (RF) techniques [8, 12] are usually involved to deal with such scenarios. However, these methods are designed for document retrieval, where relevance is the most important criterion.