Video e-commerce: large scale online video advertising based on user preference
Автор: Gunavathie. M.A., Kamalot Baavi.P., Saranya.J., Pratheepa.P., Roshini Suryadharshini.R.
Журнал: International Journal of Education and Management Engineering @ijeme
Статья в выпуске: 5 vol.8, 2018 года.
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An advertisement is a notice or announcement in a public medium promoting a product. Advertising plays significant role in the introduction of a new product in the market. It stimulates the people to purchase the product. In this paper, we propose a novel personalized Online Video Advertising System which is presented to recommend product ads from ecommerce sites to users of online video hosting. First we have to find a user preference, so we have to analyze each user’s behavior of ecommerce sites. If the user wants to buy a product, user will spend more time to view the specification at the same time, user clicks that product more number of times to view the specification, price etc. These techniques are used to find the user preference. After collecting each user preference, we have to identify the semantic association between videos and products and construct the association between the key frames and products. A multi view deep learning approach is brought to view item features in different domains. When the User plays the video the user preferred advertisements are shown in the video in proper timestamps. Thus the advertisements are displayed only based on the user’s preference and the user’s will to purchase the desired product. This has been the key feature of our project. Maintaining privacy is one of the major problems for sharing personal information through social sites. Sharing video in social media may lead to unwanted problem and less privacy. As a result we need some tools for secured transmission. For satisfying this need, we propose a system called Adaptive Privacy Policy Prediction (A3P) to enhance privacy settings for user’s data. This system provides a two-level framework for securing the data based on users browsing history on shopping site. It determines the best available privacy policy for users. Our solution relies on image classification which is associated with policies to upload images and also to user's social features. Hence by using this adaptive privacy policy our sharing of shopping and recommended videos can be secured to only desire people.
Frame split, video analysis, recommendation, user preferred advertisement, secured sharing
Короткий адрес: https://sciup.org/15015780
IDR: 15015780 | DOI: 10.5815/ijeme.2018.05.05
Список литературы Video e-commerce: large scale online video advertising based on user preference
- O. Sar Shalom, N. Koenigstein, U. Paquet, and H. P. Vanchinathan, “Beyond collaborative filtering: The list recommendation problem,” in Proc. Int. World Wide Web Conf., 2016, pp. 63–72.
- A.M. Elkahky, Y. Song, and X. He, “A multi-view deep learning approach for cross domain user modeling in recommendation systems,” in Proc. Int. World Wide Web Conf., 2015, pp. 278–288.
- T. Zhu, P. Harrington, J. Li, and L. Tang, “Bundle recommendation in ecommerce,” in Proc. Int. ACM SIGIR Conf. Res. Devlop. Inf. Retrieval, 2014, pp. 657–666.
- J. J. McAuley, C. Targett, Q. Shi, and A. van den Hengel, “Image-based recommendations on styles and substitutes,” in Proc. Int. ACM SIGIR Conf. Res. Develop Inf. Retrieval, 2015, pp. 43–52.
- A. Veit et al., “Learning visual clothing style with heterogeneous dyadic co-occurrences,” in Proc. Int. Conf. Comput. Vis., 2015, pp. 4642–4650.
- T. Mei and X.-S. Hua, “Contextual internet multimedia advertising,” Proc. IEEE, vol. 98, no. 8, pp. 1416–1433, Aug. 2010.
- T. Mei, X.-S. Hua, and S. Li, “Contextual in-image advertising,” in Proc. ACM Int. Conf. Multimedia, 2008, pp. 439–448.
- T. Mei, L. Li, X.-S. Hua, and S. Li, “Image Sense: Towards contextual image advertising,” ACM Trans. Multimedia Comput., Commun. Appl., vol. 8, no. 1, 2012, Art. no. 6.
- P. Zhou, Y. Zhou, D. Wu, and H. Jin, “Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks,” IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1217–1229, Jun. 2016.
- S. Ahern, D. Eckles, N. S. Good, S. King, M. Naaman, and R. Nair. Over-exposed?: privacy patterns and considerations in online and mobile photo sharing. In Conference on Human factors in computing systems, pages 357–366. ACM, 2007.
- A. Besmer and H. Lipford. Tagged photos: concerns, perceptions, and protections. In CHI ’09: 27th international conference extended abstracts on Human factors in computing systems, pages 4585–4590. ACM, 2009.
- L. Church, J. Anderson, J. Bonneau, and F. Stajano. Privacy stories: Confidence on privacy behaviors through end user programming. In Symposium on Usable Privacy and Security (SOUPS), 2009.
- H. Lipford, A. Besmer, and J. Watson. Understanding privacy settings in facebook with an audience view. In Proceedings of the Conference on Usability, Psychology, and Security, 2008.
- Y. Liu, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Analyzing facebook privacy settings: user expectations vs. reality. In 2011 ACM SIGCOMM conference on Internet measurement conference, IMC ’11, pages 61–70. ACM, 2011.
- K. Strater and H. Lipford. Strategies and struggles with privacy in an online social networking community. In British Computer Society Conference on Human-Computer Interaction, 2008.
- C. A. Yeung, L. Kagal, N. Gibbins, and N. Shadbolt. Providing access control to online photo albums based on tags and linked data. In Social Semantic Web: Where Web 2.0 Meets Web 3.0 at the AAAI 2009 Symposium, 2009.
