Conference Proceeding Article
Personalization, or customizing the experience of each individual user, is seen as a useful way to navigate the huge variety of choices on the Web today. A key tenet of personalization is the capacity to model user preferences. The paradigm has shifted from that of individual preferences, whereby we look at a user's past activities alone, to that of shared preferences, whereby we model the similarities in preferences between pairs of users (e.g., friends, people with similar interests). However, shared preferences are still too granular, because it assumes that a pair of users would share preferences across all items. We therefore postulate the need to pay attention to "context", which refers to the specific item on which the preferences between two users are to be estimated. In this paper, we propose a generative model for contextual agreement in preferences. For every triplet consisting of two users and an item, the model estimates both the prior probability of agreement between the two users, as well as the posterior probability of agreement with respect to the item at hand. The model parameters are estimated from ratings data. To extend the model to unseen ratings, we further propose several matrix factorization techniques focused on predicting agreement, rather than ratings. Experiments on real-life data show that our model yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
WWW '14: Proceedings of the 23rd International Conference on World Wide Web: April 7-11, 2014, Seoul, Korea
City or Country
DO, Ha Loc and LAUW, Hady Wirawan.
Modeling Contextual Agreement in Preferences. (2014). WWW '14: Proceedings of the 23rd International Conference on World Wide Web: April 7-11, 2014, Seoul, Korea. 315-326. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2014
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.