Category reformation using purchase logs
Kouga Kobayashi, Yuri Nozaki, Takayasu Fushimi, Tetsuji Satoh · 2017
In item searches on shopping sites, customers must find the items they want from among many products. As one method of narrowing down products, the customer currently selects a category and posts queries related to the item in it. However, in such narrowing-down method searches for products, the customer needs to know the category to narrow down the shopping sites. In this paper, after a customer posts a query related to a product, the shopping site recommends a word that is closely related to a new category generated from a query log, and we propose a new search method to combine category and keyword searches. Our evaluation experiment using actual data shows that the categories proposed by recommendations provide sufficient information quantity.