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Title: Role of intensity of emotions for effective personalized video recommendation: A reinforcement learning approach
Authors: Tripathi A.
Manasa D.G.
Rakshitha K.
Ashwin T.S.
Ram Mohana Reddy, Guddeti
Issue Date: 2018
Citation: Advances in Intelligent Systems and Computing, 2018, Vol.709, pp.507-517
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
URI: 10.1007/978-981-10-8633-5_50
Appears in Collections:3. Book Chapters

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