Probabilistic ranking of multi-attribute items using indifference curve

This work proposes a novel probabilistic multi-attribute item ranking framework to estimate the probability of an item being a user´s best choice and rank items accordingly. It uses indifference curve from microeconomics to model users´ personal preference, and addresses the inter-attribute tradeoff and inter-item competition issues at the same time with little information loss. The proposed framework also considers the fact that a user can only compare a few items at the same time, and models the user´s selection process as a two-step process, where the user first selects a few candidates, and then makes detailed comparison. Simulation results show that the proposed framework significantly outperforms existing multiattribute ranking algorithms in terms of ranking quality.