A sequential decision approach to ordinal preferences in recommender systems

Truyen Tran, Dinh Q. Phung, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAAAI-12 / IAAI-12 - Proceedings of the 26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference
Number of pages7
Publication statusPublished - 7 Nov 2012
Externally publishedYes
EventAAAI Conference on Artificial Intelligence 2012 - Toronto, Canada
Duration: 22 Jul 201226 Jul 2012
Conference number: 26th

Publication series

NameProceedings of the National Conference on Artificial Intelligence


ConferenceAAAI Conference on Artificial Intelligence 2012
Abbreviated titleAAAI 2012
Internet address

Cite this