Mining user trails in critiquing based recommenders
Skanda Raj Vasudevan, Sutanu Chakraborti · 2014
Critiquing based recommenders are very commonly used to help users navigate through the product space to find the required product by tweaking/critiquing one or more features. By critiquing a product, the user gives an informative feedback(i.e, which feature needs to be modified) about why they rejected a product and preferred the other one. As a user interacts with such a system, trails are left behind. We propose ways of leveraging these trails to induce preference models of items which can be used to estimate the relative utilities of products which can be used in ranking the recommendations presented to the user. The idea is to effectively complement knowledge of explicit user interactions in traditional social recommenders with knowledge implicitly obtained from trails.