ABSTRACT
A good recommendation can play a pivotal role in the human decision-making process. With the exponential rise in consumerism in this era of technology, consumers face a wide range of choices while a difficult conundrum for producers is to sell their content to specific users, which maximizes the profit. The concept of recommender systems (RSs) tries to solve this issue for both buyers and sellers alike, and the availability of a large amount of related data just makes the task less challenging. In this paper, a new model of RS is proposed that uses a probabilistic data structure (PDS), besides the traditional model, to further improve the recommendations provided to the user. As the amount of data collected is said to increase drastically over time, a PDS makes the task of storing a large amount of data and querying from the storage easier. In this paper, we focus to solve the issues of normal RS by using bloom filter a PDS to provide a better recommendation to the user.
