Product Insights: Analyzing Product Intents in Web Search
This work addresses the limited understanding of user behavior in product search for web search engines, but it is incremental as it applies existing methods to new data.
The study tackled the problem of understanding user intents in product search on web engines by analyzing Bing search logs, resulting in a taxonomy and a machine learning classifier achieving an overall F1-score of 78% for intent classification.
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.