Publication Type

Conference Proceeding Article

Publication Date

9-2016

Abstract

Users express their preferences for items in diverse forms, through their liking for items, as well as through the sequence in which they consume items. The latter, referred to as “sequential preference”, manifests itself in scenarios such as song or video playlists, topics one reads or writes about in social media, etc. The current approach to modeling sequential preferences relies primarily on the sequence information, i.e., which item follows another item. However, there are other important factors, due to either the user or the context, which may dynamically affect the way a sequence unfolds. In this work, we develop generative modeling of sequences, incorporating dynamic user-biased emission and context-biased transition for sequential preference. Experiments on publicly-available real-life datasets as well as synthetic data show significant improvements in accuracy at predicting the next item in a sequence

Keywords

sequential preference, generative model, user-biased emission, context-biased transition

Discipline

Theory and Algorithms

Research Areas

Data Management and Analytics

Publication

Machine Learning and Knowledge Discovery in Databases

First Page

145

Last Page

161

ISBN

978-3-319-46226-4

Identifier

10.1007/978-3-319-46227-1_10

Publisher

Springer

City or Country

na

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1007/978-3-319-46227-1_10

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