Abstract
A recommendation is a suggestion or proposal as to the best course of action, especially one put forward by an authoritative body. Shopping is a necessity of every human being, and when we do shop, it is definitely either the product we like or our friends like. Aim of the research paper is to give recommendation to the user based on the user’s interest in a single cross platform. Design a cross-platform layers. Currently provide the public centres. Provide a platform for mining user interest content across different social networks. In the proposed system, we develop a Web application where in the user has to subscribe by giving the users credentials along with his interest in different fields. Based on user’s priority and interest, recommendations are displayed. A cross-platform Web application which is purely based on the user interest and interactivity obtain big data based on the user interactivity and use it for future recommendations. Content is recommend based on user interest.
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Suhas, G.K., Devananda, S.N., Jagadeesh, R., Pareek, P.K., Dixit, S. (2021). Recommendation-Based Interactivity Through Cross Platform Using Big Data. In: Tavares, J.M.R.S., Chakrabarti, S., Bhattacharya, A., Ghatak, S. (eds) Emerging Technologies in Data Mining and Information Security. Lecture Notes in Networks and Systems, vol 164. Springer, Singapore. https://doi.org/10.1007/978-981-15-9774-9_60
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DOI: https://doi.org/10.1007/978-981-15-9774-9_60
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