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Permutation Models for Collaborative Ranking

journal contribution
posted on 2014-07-23, 00:00 authored by Truyen TranTruyen Tran, Svetha VenkateshSvetha Venkatesh
We study the problem of collaborative filtering where ranking information is available. Focusing on the core of the collaborative ranking process, the user and their community, we propose new models for representation of the underlying permutations and prediction of ranks. The first approach is based on the assumption that the user makes successive choice of items in a stage-wise manner. In particular, we extend the Plackett-Luce model in two ways - introducing parameter factoring to account for user-specific contribution, and modelling the latent community in a generative setting. The second approach relies on log-linear parameterisation, which relaxes the discrete-choice assumption, but makes learning and inference much more involved. We propose MCMC-based learning and inference methods and derive linear-time prediction algorithms.

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Journal

arXiv

Language

eng

Publication classification

X Not reportable, AN Other book, or book not attributed to Deakin University

Publisher

Cornell University Library

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