Learning From Ordered Sets and Applications in Collaborative Ranking
Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and orde...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Journal of Machine Learning Research (JMLR)
2012
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| Online Access: | http://www.jmlr.org/proceedings/papers/v25/ http://hdl.handle.net/20.500.11937/8284 |
| _version_ | 1848745612403539968 |
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| author | Tran, Truyen Phung, D. Venkatesh, S. |
| author2 | Hoi, S.C.H. |
| author_facet | Hoi, S.C.H. Tran, Truyen Phung, D. Venkatesh, S. |
| author_sort | Tran, Truyen |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the statespace efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods. |
| first_indexed | 2025-11-14T06:20:08Z |
| format | Conference Paper |
| id | curtin-20.500.11937-8284 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:20:08Z |
| publishDate | 2012 |
| publisher | Journal of Machine Learning Research (JMLR) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-82842023-02-02T07:57:36Z Learning From Ordered Sets and Applications in Collaborative Ranking Tran, Truyen Phung, D. Venkatesh, S. Hoi, S.C.H. Buntine, W. Ranking over sets arise when users choose between groups of items. For example, a group may be of those movies deemed 5 stars to them, or a customized tour package. It turns out, to model this data type properly, we need to investigate the general combinatorics problem of partitioning a set and ordering the subsets. Here we construct a probabilistic log-linear model over a set of ordered subsets. Inference in this combinatorial space is highly challenging: The space size approaches (N!=2)6:93145N+1 as N approaches infinity. We propose a split-and-merge Metropolis-Hastings procedure that can explore the statespace efficiently. For discovering hidden aspects in the data, we enrich the model with latent binary variables so that the posteriors can be efficiently evaluated. Finally, we evaluate the proposed model on large-scale collaborative filtering tasks and demonstrate that it is competitive against state-of-the-art methods. 2012 Conference Paper http://hdl.handle.net/20.500.11937/8284 http://www.jmlr.org/proceedings/papers/v25/ Journal of Machine Learning Research (JMLR) restricted |
| spellingShingle | Tran, Truyen Phung, D. Venkatesh, S. Learning From Ordered Sets and Applications in Collaborative Ranking |
| title | Learning From Ordered Sets and Applications in Collaborative Ranking |
| title_full | Learning From Ordered Sets and Applications in Collaborative Ranking |
| title_fullStr | Learning From Ordered Sets and Applications in Collaborative Ranking |
| title_full_unstemmed | Learning From Ordered Sets and Applications in Collaborative Ranking |
| title_short | Learning From Ordered Sets and Applications in Collaborative Ranking |
| title_sort | learning from ordered sets and applications in collaborative ranking |
| url | http://www.jmlr.org/proceedings/papers/v25/ http://hdl.handle.net/20.500.11937/8284 |