The problem of aggregating rankings from heterogeneous domains into one, all-comprising rank is very difficult in its formulation. It takes into account ranking relevance based on the domains where rankings are aggregated: books, perfumes, music, movies, and so on. Thus, the solution of having one universal function is limited, and more sophisticated methods that blend documents and domains together are more appropriate. To establish ground truth, one needs to label the results generated by a search engine with some standard categories, ranging from “perfect” to “bad.” The monotonic increasing transformation is the key concept, and it acts on the set of training data to generate a combined ranking score.

By solving a quadratic linear programming equation, one determines the ranking coefficients. The data experiments, based on queries from Yahoo! Answers, show the results and best-case scenarios for blended learning in which a discounted cumulative gain (DCG) of ten is the upper bound. The demand for vertical searches over the Web describes the need for more efficient methods to aggregate page ranks from multiple domains and display the most relevant ones. The algorithm presented in the paper shows how blended learning can be an adaptive process that helps to determine the optimal combined list of ranked items.