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|dc.contributor.author||Ram Mohana Reddy, Guddeti||-|
|dc.identifier.citation||Advances in Intelligent Systems and Computing, 2018, Vol.709, pp.507-517||en_US|
|dc.description.abstract||Development 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. 2018||en_US|
|dc.title||Role of intensity of emotions for effective personalized video recommendation: A reinforcement learning approach||en_US|
|Appears in Collections:||3. Book Chapters|
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