Recommender systems –Duplicate removal - Raghava, D. (Journal of Advanced Research in Signal Processing and Applications; Vol 2 No 2 (2020): Journal of Advanced Research in Signal Processing and Applications, 2021)
The quick development of the web, internet business and media outlets has prompted a flood in the measure of substance accessible for utilization. While this development may at first appear to be a positive wonder for the buyer, it additionally accompanies its difficulties. This impact has been named decision over-burden. Countless generally utilized Recommender Systems techniques depend on a given client's utilization history to appraise what different things might be applicable to them. Things that are anticipated to be the most favored by that particular client would then be able to be introduced to them in the structure an arranged rundown of proposals. Significantly, all through the ongoing history of RS, a large portion of the normally utilized calculations have moved toward the errand of suggestion as a static issue. For those models, separate cases of a client expending bits of substance will in general be handled autonomously from each other and with no thought for the request where the things were devoured. This paper talks about a few new strategies that developed in most recent two years. How to cite this article: Raghavaraju D. Recommender Systems - Duplicate Removal. J Adv Res Sig Proc Appl 2020; 2(2): 11-14