The Importance of Context When Recommending TV Content: Dataset and Algorithms
This work addresses the challenge of improving TV recommendations for users in varied viewing scenarios, but it is incremental as it builds on existing context-aware recommender systems by providing a new dataset and evaluation.
The paper tackled the problem of context-aware TV content recommendation by presenting a dataset of self-reported TV consumption with contextual information and evaluating genre prediction performance with and without context. The results showed notable improvements when including contextual features, with temporal and social context making significant contributions.
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.