AAAI Publications, Twenty-Sixth AAAI Conference on Artificial Intelligence

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A Sequential Decision Approach to Ordinal Preferences in Recommender Systems
Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Last modified: 2012-07-14


We propose a novel sequential decision approach to modeling ordinal ratings in collaborative filtering problems. The rating process is assumed to start from the lowest level, evaluates against the latent utility at the corresponding level and moves up until a suitable ordinal level is found. Crucial to this generative process is the underlying utility random variables that govern the generation of ratings and their modelling choices. To this end, we make a novel use of the generalised extreme value distributions, which is found to be particularly suitable for our modeling tasks and at the same time, facilitate our inference and learning procedure. The proposed approach is flexible to incorporate features from both the user and the item. We evaluate the proposed framework on three well-known datasets: MovieLens, Dating Agency and Netflix. In all cases, it is demonstrated that the proposed work is competitive against state-of-the-art collaborative filtering methods.


Ordinal regression, sequential decision, utility theory, matrix factorization, generalized extreme values

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