Abstract
The problem with recommending shows/programs on linear TV is the absence of explicit ratings from the user. Unlike video-on-demand and other online media streaming services where explicit ratings can be asked from the user, the linear TV does not support any such option. We have to rely only on the data available from the set top box to generate suitable recommendations for the linear TV viewers. The set top box data typically contains the number of views (frequency) of a particular show by a user as well as the duration of that view. In this paper, we try to leverage the feedback implicitly available from linear TV viewership details to generate explicit ratings, which then can be fed to the existing state-of-the-art recommendation algorithms, in order to provide suitable recommendations to the users. In this work, we assign different weightage to both frequency and duration of each user-show interaction pair, unlike the traditional approach in which either the frequency or the duration is considered individually. Finally, we compare the results of the different recommendation algorithms in order to justify the effectiveness of our proposed approach.
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Agarwal, A., Das, S., Das, J., Majumder, S. (2019). A Framework for Linear TV Recommendation by Leveraging Implicit Feedback. In: Alfred, R., Lim, Y., Ibrahim, A., Anthony, P. (eds) Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 481. Springer, Singapore. https://doi.org/10.1007/978-981-13-2622-6_16
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DOI: https://doi.org/10.1007/978-981-13-2622-6_16
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