Conference Proceeding Article
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
sequential preference, generative model, user-biased emission, context-biased transition
Theory and Algorithms
Data Management and Analytics
Machine Learning and Knowledge Discovery in Databases
City or Country
LE DUC TRONG; FANG, Yuan; and LAUW, Hady Wirawan.
Modeling sequential preferences with dynamic user and context factors. (2016). Machine Learning and Knowledge Discovery in Databases. 145-161. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3355
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.