Abstract
With the exponential increase in online advertising, it has become increasingly important to determine new and innovative methods to identify the right advertisement to display to the consumer. Current methods of recommendations for advertisements employed by popular streaming platforms use the implicitly and explicitly collected data of the users for recommending advertisements. These recommendations may not always be accurate, and a user could be bogged down by a huge number of ads from irrelevant domains. Our research focuses on a novel approach for advertising which utilizes object detection for recommending advertisements. In its current state, this idea is based on the frequency of objects detected in the frames of the video. The main outcome was that our recommender engine performed better in terms of the relevancy of the advertisement, when compared to existing systems, most notable of which is YouTube. We also note that the privacy of the user is also improved, since their personal data is not being collected in order to recommend advertisements. In terms of future scope, we identify some key areas of improvement, such as the further classification of the objects detected into sub domains, making for more fine-tuned recommendations, as well as factors involving the selection of videos such as quality, duration, and relevance.
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Hiriyannaiah, S., Manohar, M., Shankar, M.P., Kaustubha, D.S., Kampli, K. (2022). Object-Detection Based Recommendation Engine for Advertising Using Deep Learning. In: Singh, P.K., Wierzchoń, S.T., Chhabra, J.K., Tanwar, S. (eds) Futuristic Trends in Networks and Computing Technologies . Lecture Notes in Electrical Engineering, vol 936. Springer, Singapore. https://doi.org/10.1007/978-981-19-5037-7_42
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DOI: https://doi.org/10.1007/978-981-19-5037-7_42
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