Identifying Users From Their Rating Patterns

TL;DR

This study demonstrates that temporal rating patterns are highly effective for identifying individual users within households in a large movie rating dataset, achieving around 96% accuracy.

Contribution

The paper introduces a model that leverages temporal information over user preferences to accurately identify users in household-based rating data.

Findings

Temporal information is more useful than ratings for user identification.

Achieved approximately 96% accuracy in user identification within households.

Model outperforms baseline approaches using rating preferences.

Abstract

This paper reports on our analysis of the 2011 CAMRa Challenge dataset (Track 2) for context-aware movie recommendation systems. The train dataset comprises 4,536,891 ratings provided by 171,670 users on 23,974$ movies, as well as the household groupings of a subset of the users. The test dataset comprises 5,450 ratings for which the user label is missing, but the household label is provided. The challenge required to identify the user labels for the ratings in the test set. Our main finding is that temporal information (time labels of the ratings) is significantly more useful for achieving this objective than the user preferences (the actual ratings). Using a model that leverages on this fact, we are able to identify users within a known household with an accuracy of approximately 96% (i.e. misclassification rate around 4%).