eprintid: 3710
rev_number: 11
eprint_status: archive
userid: 411
dir: disk0/00/00/37/10
datestamp: 2019-12-05 13:58:54
lastmod: 2019-12-05 13:58:54
status_changed: 2019-12-05 13:58:54
type: conference_item
metadata_visibility: show
creators_name: Le, Duc-Trong
creators_name: Lauw, Hady W.
creators_name: Fang, Yuan
creators_id: trongld@vnu.edu.vn
creators_id: hadywlauw@smu.edu.sg
creators_id: yfang@smu.edu.sg
title: Modeling sequential preferences with dynamic user and context factors
ispublished: pub
subjects: IT
divisions: fac_fit
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 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.
date: 2016-09
date_type: published
full_text_status: public
pres_type: paper
event_title: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
event_location: Rivar De Garda, Italy
event_dates: 19-23, September 2016
event_type: conference
refereed: TRUE
citation:   Le, Duc-Trong and Lauw, Hady W. and Fang, Yuan  (2016) Modeling sequential preferences with dynamic user and context factors.  In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, 19-23, September 2016, Rivar De Garda, Italy.     
document_url: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3710/1/viewcontent.cgi_article%3D4357%26context%3Dsis_research