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 multi-attribute ranking algorithms in terms of ranking quality.