Tripathi, A.Manasa, D.G.Rakshitha, K.Ashwin, T.S.Reddy, G.2026-02-082018Advances in Intelligent Systems and Computing, 2018, Vol.709, , p. 507-517978331960485597833192764279783319419343978331923203497833199388449783642330414978331926283397881322200849783642375019978303002682021945357https://doi.org/10.1016/j.seppur.2024.130623https://idr.nitk.ac.in/handle/123456789/33943Development of artificially intelligent agents in video recommendation systems over past decade has been an active research area. In this paper, we have presented a novel hybrid approach (combining collaborative as well as content-based filtering) to create an agent which targets the intensity of emotional content present in a video for recommendation. Since cognitive preferences of a user in real world are always in a dynamic state, tracking user behavior in real time as well as the general cognitive preferences of the users toward different emotions is a key parameter for recommendation. The proposed system monitors the user interactions with the recommended video from its user interface and web camera to learn the criterion of decision-making in real time through reinforcement learning. To evaluate the proposed system, we have created our own UI, collected videos from YouTube, and applied Q-learning to train our system to effectively adapt user preferences. © Springer Nature Singapore Pte Ltd. 2018AffectivaCognitive preferencesEmotional intensitiesQ-learningReinforcement learning