System and method for making engagement offers based on observed navigation path

A method for making engagement offers comprising:

• monitoring, by a processor, a navigation path of a user navigating a website;
• making, by the processor, an offer for engagement with the user on a first web page reached by the user upon following the navigation path, wherein the offer for engagement is suggesting knowledge to the user;
• monitoring, by the processor, interaction by the user with the knowledge suggested on the first web page, wherein the knowledge is associated with a particular user intent;
• updating, by the processor, based on the interaction by the user with the knowledge suggested on the first web page, tag data associated with a second web page encountered by the user while traversing the navigation path, wherein the tag data is further associated with the particular user intent and includes a value correlated to a navigation distance to the first web page;
• detecting, by the processor, a second user landing on the second web page, wherein the second user is detected to have followed at least a portion of the navigation path;
• calculating, by the processor, a confidence level as a function of the value included as the tag data, wherein the confidence level decreases as the navigation distance correlated to the value increases, the confidence level being indicative of confidence that the second user landing on the second web page has the particular user intent and will accept, on the second web page, a same knowledge suggested on the first web page;
• anticipating, by the processor, intent of the second user landing on the second web page based on the calculated confidence level;
• generating, by the processor, a search query based on the anticipated intent of the second user;
• prior to the second user expressly requesting the knowledge suggested on the first web page, proactively identifying and suggesting the knowledge to the second user based on the generated search query, wherein the proactively identifying and suggesting the knowledge further includes:
• retrieving a classifier model for the knowledge;
• determining a relevance score based on the classifier model, wherein the relevance score is indicative of a probability that the search query is relevant to the knowledge;
• and
• outputting the suggested knowledge based on the relevance score;
• receiving, by the processor, feedback relating to the suggested knowledge;
• updating, based on the feedback, the relevance score for the suggested knowledge;
• and
• adding, by the processor, the search query as one of a positive sample or a negative sample for the classifier model, based on the feedback.