  Web search engines are frequently used to access information about products.
This has increased in recent times with the rising popularity of e-commerce.
However, there is limited understanding of what users search for and their
intents when it comes to product search on the web. In this work, we study
search logs from Bing web search engine to characterize user intents and study
user behavior for product search. We propose a taxonomy of product intents by
analyzing product search queries. This is a challenging task given that only
15%-17% of web search queries are about products. We train machine learning
classifiers with query log features to classify queries based on intent with an
overall F1-score of 78%. We further analyze various characteristics of product
search queries in terms of search metrics like dwell time, success, popularity
and session-specific information.
