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dc.contributor.authorTripathi A.-
dc.contributor.authorManasa D.G.-
dc.contributor.authorRakshitha K.-
dc.contributor.authorAshwin T.S.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.identifier.citationAdvances in Intelligent Systems and Computing, 2018, Vol.709, pp.507-517en_US
dc.description.abstractDevelopment 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. 2018en_US
dc.titleRole of intensity of emotions for effective personalized video recommendation: A reinforcement learning approachen_US
dc.typeBook Chapteren_US
Appears in Collections:3. Book Chapters

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