The Importance of Context When Recommending TV Content: Dataset and Algorithms

TL;DR

This paper introduces a new dataset with contextual information for TV content recommendation and demonstrates that incorporating context improves genre prediction accuracy.

Contribution

The paper provides a novel dataset with rich contextual data and evaluates the impact of context on improving TV genre recommendation models.

Findings

Contextual features significantly improve genre prediction accuracy.

Temporal and social contexts are particularly influential.

Including context reduces prediction error compared to contextless models.

Abstract

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions.