  Product search is generally recognized as the first and foremost stage of
online shopping and thus significant for users and retailers of e-commerce.
Most of the traditional retrieval methods use some similarity functions to
match the user's query and the document that describes a product, either
directly or in a latent vector space. However, user queries are often too
general to capture the minute details of the specific product that a user is
looking for. In this paper, we propose a novel interactive method to
effectively locate the best matching product. The method is based on the
assumption that there is a set of candidate questions for each product to be
asked. In this work, we instantiate this candidate set by making the hypothesis
that products can be discriminated by the entities that appear in the documents
associated with them. We propose a Question-based Sequential Bayesian Product
Search method, QSBPS, which directly queries users on the expected presence of
entities in the relevant product documents. The method learns the product
relevance as well as the reward of the potential questions to be asked to the
user by being trained on the search history and purchase behavior of a specific
user together with that of other users. The experimental results show that the
proposed method can greatly improve the performance of product search compared
to the state-of-the-art baselines.
