On two-way grouping by one-way topic models
No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Faculty of Information and Natural Sciences |
D4 Julkaistu kehittämis- tai tutkimusraportti taikka -selvitys
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
2009
Major/Subject
Mcode
Degree programme
Language
en
Pages
29
Series
TKK reports in information and computer science, 15
Abstract
We tackle the problem of new users or documents in collaborative filtering. Generalization over users by grouping them into user groups is beneficial when a rating is to be predicted for a relatively new document having only few observed ratings. The same applies for documents in the case of new users. We have shown earlier that if there are both new users and new documents, two-way generalization becomes necessary, and introduced a probabilistic Two-Way Model for the task. The task of finding a two-way grouping is a non-trivial combinatorial problem, which makes it computationally difficult. We suggest approximating the Two-Way Model with two URP models; one that groups users and one that groups documents. Their two predictions are combined using a product of experts model. This combination of two one-way models achieves even better prediction performance than the original Two-Way Model. This article contains the full technical details of the conference article [22].Description
Keywords
latent topic model, collaborative filtering, cold-start problem, product of experts
