Multiservice product comparison and case based recommendation system with improved reliability

Computer Science Engineering

This paper proposes a novel scheme of compressing encrypted images with auxiliary information. The content owner, encrypt the uncompressed images and generate information, which will be used for data compression and image reconstruction. The channel provider who cannot access the original content may compress the encrypt data by quantization method with optimal parameters and transmit the compressed data. At receiver side, the principal image can be restored using the compressed encrypted data and secret key. In this proposed work, an improved multi-featured based product recommendation system was built on the real time ecommerce sites. In this system, multiple web based products are analyzed and ranked by using multi-product based recommender system and multiple products from different vendors are taken with multiple product features. The proposed work gives the best solution to the users who are interested in comparing the different products. To improve scalability and efficiency in a big data environment the proposed system is implemented on Hadoop, which is widely adopted using distributed computing platform using the MapReduce parallel processing paradigm. Hence Our Applications Stands unique as it does not rely on the Single Service Provider. The Purchase phase look up for the Web services of the Products Service Provider and can make the Online Payment with the Banks from Service Provider All the Information Will be Securely and Precisely Stored in the Users Session.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.