Publication Type

Journal Article

Version

publishedVersion

Publication Date

9-2016

Abstract

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 model this, we design probabilistic models for the generation of rating differences between pairs of users across different items. Since this model also involves the estimation of rating differences on unseen items for the purpose of prediction, we further conduct a systematic analysis of matrix factorization and tensor factorization methods in this estimation, and propose a factorization model with a novel objective function of minimizing error in rating differences. Experiments on several real-life rating datasets show that our proposed model consistently yields context-specific similarity values that perform better on a prediction task than models relying on shared preferences.

Keywords

User preference, contextual agreement, generative model

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Information Systems

Volume

34

Issue

4

First Page

21:1

Last Page

33

ISSN

1046-8188

Identifier

10.1145/2854147

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/2854147

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