Content Based Filtering Algorithm for Computer Science Research Article Recommendation

computer Engineering

It is common to find recommendation systems these days since the Internet has so much digital information uploaded every day. This overload of digital information makes it hard to find the specific information needed quickly. Recommendation systems are used to offer users a list of suggestions that may be required for user based on the data they provide. The fundamental purpose of these recommendation systems is to predict what the user might be interested in. Recommendation systems can be used in various fields, such as entertainment, shopping, business, etc. With the increased number of research articles, it is important for the researchers to have a recommendation system that makes searching for relevant articles simpler for the researcher. In this paper, we describe a recommendation system that uses proposed CBRAlgorithm to suggest the top ten papers based on the concept that the user inserts. As a matter of description, this article utilizes the concept of TFIDF to implement vectorization, and it uses a cosine similarity measure to find the relevant papers along with proposed CBRAlgorithm.

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