Consensus ranking under the exponential model
This work addresses a computational challenge in ranking models for applications like recommendation systems, but it is incremental as it builds on existing exponential models.
The paper tackles the NP-hard problem of estimating the central ranking and parameters in the generalized Mallows model, showing that exact estimation is tractable when the distribution is concentrated around its mode and introducing a conjugate prior for this model class.
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new results: (1) We show that search methods can estimate both the central ranking pi0 and the model parameters theta exactly. The search is n! in the worst case, but is tractable when the true distribution is concentrated around its mode; (2) We show that the generalized Mallows model is jointly exponential in (pi0; theta), and introduce the conjugate prior for this model class; (3) The sufficient statistics are the pairwise marginal probabilities that item i is preferred to item j. Preliminary experiments confirm the theoretical predictions and compare the new algorithm and existing heuristics.