Abstract: Features extracted from network browser pages and/or network search queries are leveraged to facilitate in detecting a user's browsing and/or searching intent. Machine learning classifiers constructed from these features automatically detect a user's online commercial intention (OCI). A user's intention can be commercial or non-commercial, with commercial intentions being informational or transactional. In one instance, an OCI ranking mechanism is employed with a search engine to facilitate in providing search results that are ranked according to a user's intention. This also provides a means to match purchasing advertisements with potential customers who are more than likely ready to make a purchase (transactional stage). Additionally, informational advertisements can be matched to users who are researching a potential purchase (informational stage).

Abstract: Features extracted from network browser pages and/or network search queries are leveraged to facilitate in detecting a user's browsing and/or searching intent. Machine learning classifiers constructed from these features automatically detect a user's online commercial intention (OCI). A user's intention can be commercial or non-commercial, with commercial intentions being informational or transactional. In one instance, an OCI ranking mechanism is employed with a search engine to facilitate in providing search results that are ranked according to a user's intention. This also provides a means to match purchasing advertisements with potential customers who are more than likely ready to make a purchase (transactional stage). Additionally, informational advertisements can be matched to users who are researching a potential purchase (informational stage).