Using Audio-Derived Affective Offset to Enhance TV Recommendation

This paper introduces the concept of affective offset, which is the difference between a user's perceived affective state and the affective annotation of the content they wish to see. We show how this affective offset can be used within a framework for providing recommendations for TV programs. First a user's mood profile is determined using 12-class audio-based emotion classifications . An initial TV content item is then displayed to the user based on the extracted mood profile. The user has the option to either accept the recommendation, or to critique the item once or several times, by navigating the emotion space to request an alternative match. The final match is then compared to the initial match, in terms of the difference in the items' affective parameterization . This offset is then utilized in future recommendation sessions. The system was evaluated by eliciting three different moods in 22 separate users and examining the influence of applying affective offset to the users' sessions. Results show that, in the case when affective offset was applied, better user satisfaction was achieved: the average ratings went from 7.80 up to 8.65, with an average decrease in the number of critiquing cycles which went from 29.53 down to 14.39.