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Physical Sciences and Mathematics Commons

Open Access. Powered by Scholars. Published by Universities.®

Databases and Information Systems

2016

Series

Generative model

Articles 1 - 2 of 2

Full-Text Articles in Physical Sciences and Mathematics

Probabilistic Models For Contextual Agreement In Preferences, Loc Do, Hady W. Lauw Sep 2016

Probabilistic Models For Contextual Agreement In Preferences, Loc Do, Hady W. Lauw

Research Collection School Of Computing and Information Systems

The long-tail theory for consumer demand implies the need for more accurate personalization technologies to target items to the users who most desire them. A key tenet of personalization is the capacity to model user preferences. Most of the previous work on recommendation and personalization has focused primarily on individual preferences. While some focus on shared preferences between pairs of users, they assume that the same similarity value applies to all items. Here we investigate the notion of "context," hypothesizing that while two users may agree on their preferences on some items, they may also disagree on other items. To …


Modeling Sequential Preferences With Dynamic User And Context Factors, Duc Trong Le, Yuan Fang, Hady W. Lauw Sep 2016

Modeling Sequential Preferences With Dynamic User And Context Factors, Duc Trong Le, Yuan Fang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

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 …