Mapping user search queries to product categories

Abstract

Gathering detailed information about user product interests is becoming increasingly important for online advertisers. However, when gathering this information, maintaining the privacy of online users is a concern. This research is part of a larger project aiming to provide privacy preserving advertising. Specifically, this research aims to provide a method for mapping user search queries to actual product categories while preserving the users' privacy by storing user information on the local host. Product information gathered from two large shopping websites and real user search queries from a log file are used to match user search queries with the most relevant product categories. In matching search queries to product categories, we explore several issues including the algorithm used to rank product categories, the index size, which fields in the index are searched (product description or product name), and what type of product categories are used. Our findings indicate that the most successful algorithms on the user's computer, which preserve privacy, can match the results of those where information is sent to a central server. In addition, the description field of products is the most useful, particularly when searched as a phrase. Having specific fine-grained product categories would help advertisers, search engines and marketers by providing them more information about users while preserving user privacy.