TY  - CONF
ID  - SisLab3710
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3710/
A1  - Le, Duc-Trong
A1  - Lauw, Hady W.
A1  - Fang, Yuan
Y1  - 2016/09//
N2  - 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 a?ect 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.
TI  - Modeling sequential preferences with dynamic user and context factors
M2  - Rivar De Garda, Italy
AV  - public
T2  - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
ER  -